CN114285504B - Large-scale wireless energy supply backscattering communication network transmission performance research method - Google Patents
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Abstract
The embodiment of the disclosure relates to a large-scale wireless energy-supply backscattering communication network transmission performance research method. The method comprises the following steps: based on random geometry theory, establishing a large-scale wireless energy-supply backscattering communication network topology model; calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model; calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio; and constructing a transmission performance maximization model based on the transmission performance, calculating the lower bound of the optimal backscattering coefficient under the instantaneous channel state information, and solving an expected value for the lower bound. The embodiment of the disclosure can be used for accurately estimating the network transmission performance and analyzing the influence of network parameters on the successful transmission probability based on the obtained network transmission performance expression, and particularly provides a reference for the transmission performance analysis of an actual system. In addition, based on the obtained suboptimal backscattering coefficient, the method can be used for realizing suboptimal successful transmission probability of a backscattering user, improving network transmission performance and providing reference for transmission performance optimization design of an actual system.
Description
Technical Field
The disclosure relates to the technical field of mobile communication, in particular to a method for researching transmission performance of a large-scale wireless energy-supply backscatter communication network.
Background
Under the condition of large-scale Internet of things, backscatter communication is widely focused, and the Internet of things node is allowed to modulate own information on an incident signal through a low-speed simple modulation scheme, and meanwhile, energy is collected from the incident signal to maintain own circuit operation, so that low-power consumption passive information transmission and energy self-maintenance are realized. Therefore, the backscattering communication is an effective technical scheme for realizing the deployment of the large-scale Internet of things, and has great research value and development prospect.
The backscatter communications can be categorized into wireless powered backscatter communications and ambient backscatter communications, depending on whether the incident signal is from a dedicated radio frequency power station or an ambient radio frequency signal source. At present, basic theoretical research on point-to-point backscatter communication networks has been greatly developed and achieved. However, these works ignore the spatial randomness of the network node locations and their research results are not applicable to large-scale backscatter communication networks. The large-scale backscatter communication network fully considers the spatial randomness of the node position in actual deployment, and in order to cope with the dynamic spatial position of the node, a random geometric theory is generally needed to be used, and the random geometric theory can obtain a performance analysis result which is easy to process under the condition of combining the network topology position randomness. Random geometry has been widely used by far in mathematical modeling and performance analysis of backscatter communication networks, and has resulted in some significant and valuable research effort.
In the above technical solution, the performance research work of the existing large-scale wireless energy-supply backscatter communication network has three limitations:
(1) Existing work assumes that each power station powers only one backscatter user through energy beam forming, which results in excessive deployment and maintenance costs. Conversely, if the power station simultaneously powers multiple backscatter users via isotropic wireless power transfer, the cost of equipment and power consumption will be greatly reduced, thereby further facilitating widespread use in practice;
(2) The existing work ignores the small scale fading of wireless power transfer links and the non-linear nature of the energy harvesting circuitry. An energy interruption occurs when the energy collected by the backscatter user is insufficient to maintain operation of the own circuit. Since small-scale fading and accurate energy collection models can have a great influence on the power collected by the back-scattered users, it is necessary to consider the nonlinear energy collection models of the small-scale fading and back-scattered users of the wireless power transmission link in performance studies;
(3) Existing work assumes perfect successive interference cancellation at the gateway to handle the interference caused by the power station, however, perfect successive interference cancellation assumptions are difficult to implement in practical systems. It is therefore necessary to consider imperfect successive interference cancellation at the gateway.
Accordingly, there is a need to provide a new solution to ameliorate one or more of the problems presented in the above solutions.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
It is an object of the present disclosure to provide a method for studying transmission performance of a large-scale wireless-powered backscatter communication network, and further overcome, at least in part, one or more of the problems due to the limitations and disadvantages of the related art.
According to the embodiment of the disclosure, a method for researching transmission performance of a large-scale wireless energy-supply backscatter communication network is provided, and comprises the following steps:
based on random geometry theory, establishing a large-scale wireless energy-supply backscattering communication network topology model;
calculating an effective signal-to-interference-and-noise ratio according to the network topology model and a standard power loss propagation model;
calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio;
and constructing a transmission performance maximization model based on the transmission performance, calculating the lower bound of the optimal backscattering coefficient under the instantaneous channel state information, and solving an expected value for the lower bound to obtain the suboptimal backscattering coefficient.
In an embodiment of the disclosure, the step of establishing a topology model of the large-scale wireless-powered backscatter communication network based on random geometry includes:
the network topology model consists of a power station obeying the poisson point process, a back scattering user and a gateway; wherein the uniform poisson point procedure for modeling the power stations, the scattering users and the gateway can be expressed as: phi p ＝{p i ,i＝0,1,2,...}、Φ u ＝{u i I=0, 1,2,..} and Φ g ＝{g i I=0, 1,2,..}, wherein p i Indicating the position of the power station, u i Indicating the position of the backscatter user g i Representing the location of the gateway and using lambda p Representing the density, lambda, of the power station u Represents the density of the backscattered user, lambda g Representing the density of the gateway.
In an embodiment of the disclosure, before the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model, the method further includes:
dividing the gateway into gateway cells based on the nearest association principle of the back scattering users and the gateway, and calculating the channel access probability of the back scattering users in the gateway cells, wherein the channel access probability is as follows:
wherein b=3.575;E[N u ]is the average number of users served by the gateway; />Is a standard gamma function, C is the total number of channels available, and n is the current number of channels.
In an embodiment of the disclosure, before the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model, the method further includes:
taking the back scattering user at the original point as a typical back scattering user, taking a gateway nearest to the typical back scattering user as a target gateway, and respectively calculating a probability density function of the distance between the typical back scattering user and the target gateway and the power received by the typical back scattering user from the power station, wherein the probability density function of the distance between the typical back scattering user and the target gateway is as follows:
where r is a typical backscatter user u 0 With target gateway g 0 Distance, lambda g Is gateway density;
the typical backscatter user receives power from the power station as:
wherein,indicating that the power station is on subchannel c k Transmit power on, C d ＝{c k K=1, 2., where, C represents a set of orthogonal channels, P B The total transmit power of the power station on the C orthogonal channels; alpha is the path loss index of the link; />And p i -u 0 I indicates the power stations p, respectively i With typical backscatter user u 0 Small scale fading gain and distance between.
In an embodiment of the disclosure, the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model further includes:
when the power received by the typical backscatter user from the power station is greater than the power consumption of a circuit of the power station, calculating the total power received by the target gateway, wherein the total power received by the target gateway is as follows:
wherein,representing the power of the backscattered signal, < >>Representing aggregate interference I from backscattered users u ，/>Representing aggregate interference I from a power station p Beta is the backscattering coefficient of the backscattering user; phi u (c k ) To use sub-channel c k And the collected energy is sufficient to maintain a set of backscatter users of their own circuit operation; />For interfering with backscatter user u l Slave power station p i Received power; />And->Respectively represent power stations p i With interfering backscatter user u l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 Small scale fading gains in between; ||p i -u l ||＝||X il ||、||u 0 -g 0 ||＝||R||、||u l -g 0 ||＝||X l I to I p i -g 0 ||＝||X i I indicates the power stations p, respectively i With interfering backscatter user u l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 A distance therebetween; sigma (sigma) 2 Is the additive white gaussian noise power at the target gateway.
In an embodiment of the disclosure, the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model further includes:
and calculating the signal-to-interference-and-noise ratio of the typical backscatter user according to the total power received by the target gateway, wherein the signal-to-interference-and-noise ratio is as follows:
wherein I is p And I u Aggregate interference from power stations and backscatter users, respectively; and xi is the interference cancellation coefficient.
In an embodiment of the disclosure, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
obtaining the aggregate interference I from the power station according to the signal-to-interference-plus-noise ratio p And aggregate interference I from the backscattered user u Is transformed by Laplace of the aggregate interference I p Interference I with said aggregation u The laplace transform of (a) is as follows:
wherein,P at the power collected for the backscatter user is greater than the probability of circuit power consumption, i.e., the activation probability of the backscatter user.
In an embodiment of the disclosure, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
combining the random geometry theory according to the polymeric dryInterference I p And the aggregate interference I u The Laplace transform of the backscattering user is obtained, and the activation probability is:
wherein,for typical backscatter user u 0 Total power collectable, +.>A mapping function for a nonlinear energy collection model; p (P) c Power consumption for the backscatter circuit; />a e And b e S is a fixed parameter related to the non-linear energy harvesting model 0 For sensitivity threshold, E max Is the maximum collectable power.
In an embodiment of the disclosure, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
combining the random geometric theory, and obtaining the successful transmission probability of the typical backscatter user according to the activation probability as follows:
wherein,R th and B is the transmission rate and bandwidth respectively, is->Probability density function of (a).
In an embodiment of the disclosure, the maximization model of the transmission performance is as follows:
the lower bound of the optimized backscatter coefficient is:
wherein,
the sub-optimal backscattering coefficient is:
wherein,m is a parameter for balancing the complexity and the accuracy in the Gaussian Chebyshev inequality, and the value range is M more than or equal to 1.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
in one embodiment of the disclosure, through the above method for researching transmission performance of a backscatter communication network, on one hand, a network topology model is established based on a random geometry theory, an effective signal-to-interference-plus-noise ratio is calculated according to the network topology model and a standard power loss propagation model, and transmission performance of a fit actual network environment, that is, successful transmission probability of a backscatter user is obtained according to the effective signal-to-interference-plus-noise ratio, and the successful transmission probability can be used for accurately estimating network transmission performance and analyzing influence of network parameters on the successful transmission probability, and especially provides a reference for transmission performance analysis of an actual system. On the other hand, based on the obtained transmission performance, the backscattering coefficient is further designed, and the suboptimal backscattering coefficient is obtained, and can be used for realizing suboptimal successful transmission probability of a backscattering user, improving network transmission performance, and particularly providing reference for the transmission performance optimization design of an actual system.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 schematically illustrates a flow chart of a method of transmission performance study of a large-scale wireless-powered backscatter communications network in an exemplary embodiment of the present disclosure;
FIG. 2 schematically illustrates a network model schematic diagram in an exemplary embodiment of the present disclosure;
fig. 3 schematically illustrates a network topology diagram in an exemplary embodiment of the present disclosure;
fig. 4 schematically illustrates a schematic diagram of successful transmission probability and interference cancellation coefficients in an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a graph of successful transmission probability versus backscatter coefficient in an exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a relationship between successful transmission probability and interference cancellation coefficient under different schemes in an exemplary embodiment of the present disclosure;
fig. 7 schematically illustrates a schematic diagram of successful transmission probabilities for different circuit power losses for an employed scheme and an exhaustive search scheme in accordance with an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In this example embodiment, a method for researching transmission performance of a large-scale wireless energy-supply backscatter communication network is provided, and referring to fig. 1, the method may include:
step S101: based on random geometry theory, establishing a large-scale wireless energy-supply backscattering communication network topology model;
step S102: calculating an effective signal-to-interference-and-noise ratio according to the network topology model and a standard power loss propagation model;
step S103: calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio;
step S104: and constructing a transmission performance maximization model based on the transmission performance, calculating the lower bound of the optimal backscattering coefficient under the instantaneous channel state information, and solving an expected value for the lower bound to obtain the suboptimal backscattering coefficient.
According to the large-scale wireless energy-supply backscattering communication network transmission performance research method, on one hand, a network topology model is established based on a random geometric theory, an effective signal-to-interference-plus-noise ratio is calculated according to the network topology model and a standard power loss propagation model, and the transmission performance which is fit with an actual network environment, namely the successful transmission probability of a backscattering user, is obtained according to the effective signal-to-interference-plus-noise ratio, and can be used for accurately estimating the network transmission performance and analyzing the influence of network parameters on the successful transmission probability, and particularly, a reference is provided for the transmission performance analysis of an actual system. On the other hand, based on the obtained transmission performance, the backscattering coefficient is further designed, and the suboptimal backscattering coefficient is obtained, and can be used for realizing suboptimal successful transmission probability of a backscattering user, improving network transmission performance, and particularly providing reference for the transmission performance optimization design of an actual system.
Next, each step of the above-described method in the present exemplary embodiment will be described in more detail with reference to fig. 1 to 7.
In step S101, a topology model of a large-scale wireless-powered backscatter communications network is established based on random geometry theory.
Optionally, in some embodiments, the step of establishing a topology model of the large-scale wireless-powered backscatter communications network based on random geometry includes:
the network topology model consists of a power station obeying the poisson point process, a back scattering user and a gateway; wherein the uniform poisson point procedure for modeling the power stations, the scattering users and the gateway can be expressed as: phi p ＝{p i ,i＝0,1,2,...}、Φ u ＝{u i I=0, 1,2,..} and Φ g ＝{g i I=0, 1,2,..}, wherein p i Indicating the position of the power station, u i Indicating the position of the backscatter user g i Representing the location of the gateway and using lambda p Representing the density, lambda, of the power station u Represents the density of the backscattered user, lambda g Representing the density of the gateway.
Optionally, in some embodiments, the step of calculating an effective signal to interference plus noise ratio according to the network topology model and a standard power loss propagation model further includes:
dividing the gateway into gateway cells based on the nearest association principle of the back scattering users and the gateway, and calculating the channel access probability of the back scattering users in the gateway cells, wherein the channel access probability is as follows:
wherein b=3.575;E[N u ]is the average number of users served by the gateway; />Is a standard gamma function, C is the total number of channels available, and n is the current number of channels.
In particular, referring to fig. 2, a network is made up of a plurality of power stations, energy-limited backscatter users, and gateways. The network may be divided into gateway cells by taking into account the principle of recent association between the backscatter user and the gateway. The power station is equipped with an isotropic antenna, and the backscatter user is equipped with an energy harvesting circuit and a backscatter circuit, the power station broadcasts an energy signal out in an isotropic wireless power transfer mode, the backscatter user modulates own information onto the power station's energy signal and reflects the modulated signal to the nearest gateway, while harvesting energy from the power station's energy signal to maintain own circuit operation. The whole available bandwidth of the network is formed by C orthogonal channels C d ＝{c k K=1, 2,..c } the power station operates over a wide frequency band of the set of orthogonal channels. It is assumed that in each gateway cell, the gateway uses a random spectrum access strategy to serve only one backscatter user on each channel. Using standard power loss propagation model hr -α Describing the fading characteristics of a wireless channel, where h represents a small scale fading gain, r -α Denote large-scale path loss, r denote propagation distance, and α denote path loss index. All channels are assumed to be quasi-static, independent, co-distributed. Small scale fading is modeled as rayleigh fading that follows an exponential distribution, i.e., h-exp (1).
Referring to fig. 3, the five-pointed star represents the power station, the dots represent the backscatter users, the triangles represent the gateways, and the locations of the three network nodes are modeled as a uniform poisson process with a distribution density ranging from large to small: backscatter users, power stations, gateways. By means of the Thiessen polygonal Voronoi diagram, the network is divided into gateway cells.
Optionally, in some embodiments, the step of calculating an effective signal to interference plus noise ratio according to the network topology model and a standard power loss propagation model further includes:
taking the back scattering user at the original point as a typical back scattering user, taking a gateway which is far away from the typical back scattering user as a target gateway, and respectively calculating a probability density function of the distance between the typical back scattering user and the target gateway and the power received by the typical back scattering user from the power station, wherein the probability density function of the distance between the typical back scattering user and the target gateway is as follows:
where r is a typical backscatter user u 0 With target gateway g 0 Distance, lambda g Is gateway density;
the typical backscatter user receives power from the power station as:
wherein,indicating that the power station is on subchannel c k Transmit power on, C d ＝{c k K=1, 2., where, C represents a set of orthogonal channels, P B The total transmit power of the power station on the C orthogonal channels; alpha is the path loss index of the link;respectively represent power stations p i With typical backscatter user u 0 Small scale fading gain and distance between.
In particular, the calculation of the power received by a typical backscatter user from a power station is based on the power station operating over a wide band of available C orthogonal channels. During information transmission, a typical backscatter user divides the power signal collected from all power stations into two parts, one part being a carrier wave on which its own information is modulated and the modulated signal is reflected to the target gateway, the other part being used for energy harvesting to maintain its own circuit operation.
In step S102, an effective signal-to-interference-and-noise ratio is calculated from the network topology model and a standard power loss propagation model.
Optionally, in some embodiments, the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model further includes:
when the power received by the typical backscatter user from the power station is greater than the power consumption of a circuit of the power station, calculating the total power received by the target gateway, wherein the total power received by the target gateway is as follows:
wherein,representing the power of the backscattered signal, < >>Representing aggregate interference I from backscattered users u ，/>Representing aggregate interference I from a power station p Beta is the backscattering coefficient of the backscattering user; phi u (c k ) To use sub-channel c k And the collected energy is sufficient to maintain a set of backscatter users of their own circuit operation; />For interfering with backscatter user u l Slave power station p i Received power; />And->Respectively represent power stations p i With interfering backscatter user u l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 Small scale fading gains in between; ||p i -u l ||＝||X il ||、||u 0 -g 0 ||＝||R||、||u l -g 0 ||＝||X l I to I p i -g 0 ||＝||X i I indicates the power stations p, respectively i With interfering backscatter user u l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 A distance therebetween; sigma (sigma) 2 Is the additive white gaussian noise power at the target gateway.
In particular, according to the mechanism of backscatter communications, a typical backscatter user will receive a power signal according to a backscatter coefficient βIs divided into two parts, namely->For energy harvesting, +.>For backscatter information transmission. When the power collected by a typical backscatter user from a power station is greater than the power consumption of its own circuitry, i.e., activated, the total power received by the target gateway is calculated according to a standard power loss propagation model. Calculating the total power received by the target gateway based on a standard power loss propagation model, wherein the total power received by the target gateway is calculated by the power of the back scattered signal,Aggregate interference from the backscattered user, aggregate interference from the power station, and additive white gaussian noise power at the target gateway. The total power received by the target gateway is calculated to facilitate subsequent calculations of the signal-to-interference-and-noise ratio of a typical backscatter user.
Optionally, in some embodiments, the step of calculating an effective signal-to-interference-and-noise ratio according to the network topology model and the standard power loss propagation model further includes:
and calculating the signal-to-interference-and-noise ratio of the typical backscatter user according to the total power received by the target gateway, wherein the signal-to-interference-and-noise ratio is as follows:
wherein I is p And I u Aggregate interference from power stations and backscatter users, respectively; and xi is the interference cancellation coefficient. In step S103, the transmission performance of the network is calculated according to the effective signal-to-interference-and-noise ratio.
Specifically, based on the total power received by the target gateway, the gateway adopts imperfect continuous elimination to obtain the signal-to-interference-and-noise ratio of the target gateway for decoding the typical backscatter user, so as to calculate the transmission performance of the network subsequently.
Optionally, in some embodiments, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
obtaining the aggregate interference I from the power station according to the signal-to-interference-plus-noise ratio p And aggregate interference I from the backscattered user u Is transformed by Laplace of the aggregate interference I p Interference I with said aggregation u The laplace transform of (a) is as follows:
wherein,P at the power collected for the backscatter user is greater than the probability of circuit power consumption, i.e., the activation probability of the backscatter user.
Specifically, for the subsequent calculation of network transmission performance, based on the obtained signal-to-interference-and-noise ratio, the aggregated interference from the power station and the aggregated interference from the backscatter user are subjected to laplace transformation.
Optionally, in some embodiments, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
combining the random geometry theory according to the polymerization interference I p And the aggregate interference I u The Laplace transformation of the backscattering user is obtained, and the activation probability is as follows:
wherein,for typical backscatter user u 0 Total power collectable, +.>A mapping function for a nonlinear energy collection model; p (P) c Power consumption for the backscatter circuit; />a e And b e S is a fixed parameter related to the non-linear energy harvesting model 0 For sensitivity threshold, E max Is the maximum collectable power.
In particular, for the subsequent calculation of the transmission performance of a typical backscatter user,i.e. probability of successful transmission, according to random geometry theory, activation probability definition and aggregate interference I from power stations p And aggregate interference I from backscatter users u The laplace transform of (a) to obtain the activation probability of the backscatter user. Wherein the activation probability is the probability that the power collected by the backscatter user is greater than the circuit power consumption. And under the typical pathloss α=4 condition, the activation probability can be recalculated as:
optionally, in some embodiments, the step of calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio includes:
combining the random geometric theory, and obtaining the successful transmission probability of the typical backscatter user according to the activation probability as follows:
wherein,R th and B is the transmission rate and bandwidth respectively, is->Probability density function of (a).
Specifically, according to the definition of the activation probability and the successful transmission probability, the successful transmission probability of a typical backscatter user, namely the successful transmission performance of the network, is obtained by combining a random geometry theory. The obtained successful transmission probability can be used for accurately estimating the network transmission performance and analyzing the influence of network parameters on the successful transmission probability, and particularly provides a reference for the transmission performance analysis of an actual system. The probability of successful transmission is the probability that the signal-to-interference-and-noise ratio of the gateway is larger than the signal-to-interference-and-noise ratio threshold value and the power collected by the back scattering user is larger than the power consumption of the circuit. And under the typical path loss α=4 condition, the successful transmission probability can be recalculated as:
wherein,m is a parameter for balancing the complexity and the accuracy in the Gaussian Chebyshev inequality, and the value range is M more than or equal to 1.
Step S104: and constructing a maximum model of transmission performance by optimizing the backscattering coefficient, calculating the lower bound of the optimal backscattering coefficient under the instantaneous channel state information, and solving an expected value for the lower bound to obtain a suboptimal backscattering coefficient.
Optionally, in some embodiments, the maximization model of the transmission performance is as follows:
the lower bound of the optimized backscatter coefficient is:
wherein,
the sub-optimal backscattering coefficient is:
wherein,m is a parameter for balancing the complexity and the accuracy in the Gaussian Chebyshev inequality, and the value range is M more than or equal to 1.
Specifically, there is a unique optimal solution for the maximization model of transmission performance, but I u The method is related to the backscattering coefficient, so that a closed solution of the optimal backscattering coefficient in the instantaneous channel state is difficult to solve, a lower bound of the optimal backscattering coefficient is obtained, an expected value is taken from the lower bound of the optimal backscattering coefficient, a suboptimal backscattering coefficient is obtained, the suboptimal backscattering coefficient can be used for realizing suboptimal successful transmission probability of a backscattering user, improving network transmission performance, and providing reference for the optimal design of the transmission performance of an actual system.
According to the method for researching the transmission performance of the back-scattering communication network, on one hand, a network topology model is established based on random geometric theory, an effective signal-to-interference-and-noise ratio is calculated according to the network topology model and a standard power loss propagation model, and the transmission performance which is attached to an actual network environment, namely the successful transmission probability of a back-scattering user, is obtained according to the effective signal-to-interference-and-noise ratio, and can be used for accurately estimating the network transmission performance and analyzing the influence of network parameters on the successful transmission probability, and particularly, a reference is provided for the transmission performance analysis of an actual system. On the other hand, based on the obtained transmission performance, the backscattering coefficient is further designed, and the suboptimal backscattering coefficient is obtained, and can be used for realizing suboptimal successful transmission probability of a backscattering user, improving network transmission performance, and particularly providing reference for the transmission performance optimization design of an actual system.
The embodiments of the present disclosure are further described below by simulation experiments.
According to the embodiment of the disclosure, a simulation experiment is carried out on a large-scale wireless energy-supply backscattering communication network transmission performance research method in a square area of 100m multiplied by 100 m. Table 1 shows the simulation parameters involved, and the specific contents are as follows:
table 1: simulation parameters
By bringing the simulation parameters into the acquired expression, the probability of successful transmission and sub-optimal backscatter coefficients for the backscatter user in the area under consideration can be readily obtained. The correctness of the successful transmission probability and the suboptimal backscattering coefficient obtained in the embodiment of the present disclosure is proved through simulation according to the parameter settings in table 1.
Fig. 4 shows the relation between successful transmission probability and interference cancellation coefficient at different transmission rates. It can be seen from the figure that the theoretical value of the successful transmission probability is very close to the simulation value, which indicates the correctness of the successful transmission probability expression obtained by the embodiments of the present disclosure. The following phenomenon can also be observed from fig. 4. First, the probability of successful transmission decreases with increasing interference cancellation coefficients, because the amount of interference residuals caused by imperfect interference cancellation increases, resulting in a decrease in signal-to-interference-and-noise ratio. Second, a smaller transmission rate corresponds to a higher probability of successful transmission given the interference cancellation coefficient, since the signal-to-interference-and-noise ratio threshold decreases with decreasing transmission rate. Finally, the linear energy harvesting model always produces too high a successful transmission probability value compared to the nonlinear energy harvesting model, which suggests that the use of the nonlinear energy harvesting model by embodiments of the present disclosure is a correct choice.
Fig. 5 shows the probability of successful transmission versus the backscatter coefficient for different interference cancellation coefficients. It can be seen from the figure that as the backscatter coefficient increases, the probability of successful transmission increases and then decreases, which means that there is an optimal backscatter coefficient that maximizes the probability of successful transmission. This phenomenon can be explained as: when the backscatter coefficient is small, for the power received by the backscatter user, where the fraction for backscatter information transmission is less than the fraction for energy collection, therefore the probability of successful transmission is dominated by the backscatter information transmission, resulting in an increase in the probability of successful transmission with an increase in the backscatter coefficient; when the backscatter coefficient is large, for the power received by the backscatter user, the fraction used for transmission of the backscatter information is greater than the fraction used for energy collection, and therefore the probability of successful transmission is dominated by the backscatter energy collection, resulting in a decrease in the probability of successful transmission with increasing backscatter coefficient. Under three interference cancellation coefficients, the optimal backscatter coefficient and optimal successful transmission probability obtained by the exhaustive search scheme are shown as marks in fig. 5, and the suboptimal backscatter coefficient and suboptimal successful transmission probability designed by the embodiment of the present disclosure are calculated as: 0.1400 and 0.8304, 0.3417 and 0.7562 and 0.6496 and 0.6165. As can be seen from fig. 5, the sub-optimal backscatter coefficient increases with increasing interference cancellation coefficient and the optimal backscatter coefficient increases with increasing interference cancellation coefficient. Furthermore, the optimal backscattering coefficient increases with increasing interference cancellation coefficient. This is because the signal-to-interference-plus-noise ratio decreases as the interference cancellation coefficient increases, and in order to maximize the probability of successful transmission, the backscatter coefficient needs to be increased to increase the fraction for energy harvesting.
To verify whether the sub-optimal backscatter coefficients of the employed scheme design can effectively promote successful transmission probabilities, fig. 6 compares the successful transmission probabilities achieved by four different schemes, including the employed scheme, the exhaustive search scheme, the fixed backscatter coefficient scheme, and the random backscatter coefficient scheme. It can be seen from the figure that the optimal successful transmission probability can be realized by the exhaustive search scheme, the suboptimal successful transmission probability can be realized by the adopted scheme, and the successful transmission probability realized by the fixed backscattering coefficient scheme and the random backscattering coefficient scheme is inferior to the scheme adopted by the embodiment of the disclosure, which proves that the suboptimal successful transmission probability can be realized by the suboptimal backscattering coefficient designed by the embodiment of the disclosure.
Fig. 7 shows the probability of successful transmission at different circuit power losses for the scheme employed versus the exhaustive search scheme at different transmission rates. As can be seen from fig. 7, the embodiment of the present disclosure is employed to always achieve a suboptimal probability of successful transmission at different transmission rates. For visual purposes, table 2 lists the sub-optimal backscatter coefficients and sub-optimal successful transmission probabilities, as well as the optimal backscatter coefficients and optimal successful transmission probabilities for an exhaustive search scheme search. It can be seen from table 2 that the sub-optimal backscatter coefficients and the optimal backscatter coefficients are very close, as are the sub-optimal successful transmission probabilities. Although an exhaustive search scheme can achieve optimal successful transmission probability, the implementation complexity is high and the search time is long. In contrast, embodiments of the present disclosure may achieve a sub-optimal successful transmission probability very close to optimal with less complexity and less computation time, thereby increasing the successful transmission probability.
Table 2 comparison of the example scheme of the present disclosure with the poor search scheme
The simulation analysis verifies the correctness of the successful transmission probability expression and the suboptimal backscattering coefficient, and simultaneously researches the relationship between different network parameters and the successful transmission probability. The result shows that the successful transmission probability calculated by the embodiment of the disclosure can accurately estimate the network transmission performance; the probability of successful transmission can be effectively improved by selecting a proper backscattering coefficient or reducing an interference elimination coefficient; the designed suboptimal backscattering coefficient can realize suboptimal successful transmission probability, thereby enhancing network transmission performance.
The method for researching transmission performance of the backscatter communication network provided by the embodiment of the disclosure is an accurate calculation method for successful transmission probability and a suboptimal backscatter coefficient design method, can effectively estimate and promote successful transmission probability of a large-scale wireless energy-supply backscatter communication network, and has reference value for deployment planning and performance research of an actual network.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (9)
1. A method for studying transmission performance of a large-scale wireless energy-supply backscatter communication network, the method comprising:
based on random geometry theory, establishing a network topology model of large-scale wireless energy-supply backscatter communication, comprising:
the network topology model consists of a power station obeying the poisson point process, a back scattering user and a gateway; wherein the uniform poisson point procedure for modeling the power stations, the scattering users and the gateway can be expressed as: phi p ＝{p i ,i＝0,1,2,...}、Φ u ＝{u i I=0, 1,2,..} and Φ g ＝{g i I=0, 1,2,..}, wherein p i Indicating the position of the power station, u i Indicating the position of the backscatter user g i Representing the location of the gateway and using lambda p Representing the density, lambda, of the power station u Represents the density of the backscattered user, lambda g Representing the density of the gateway;
calculating an effective signal-to-interference-and-noise ratio according to the network topology model and a standard power loss propagation model;
calculating the transmission performance of the network according to the effective signal-to-interference-and-noise ratio;
and constructing a transmission performance maximization model based on the transmission performance, calculating the lower bound of the optimal backscattering coefficient under the instantaneous channel state information, and solving an expected value for the lower bound to obtain the suboptimal backscattering coefficient.
2. The method for studying transmission performance of a large-scale wireless-powered backscatter communication network of claim 1, wherein prior to the step of calculating an effective signal-to-interference-and-noise ratio based on the network topology model and a standard power loss propagation model, further comprising:
dividing the gateway into gateway cells based on the nearest association principle of the back scattering users and the gateway, and calculating the channel access probability of the back scattering users in the gateway cells, wherein the channel access probability is as follows:
wherein b=3.575;E[N u ]is the average number of users served by the gateway; />Is a standard gamma function, C is the total number of channels available, and n is the current number of channels.
3. The method for studying transmission performance of a large-scale wireless-powered backscatter communication network of claim 2, wherein prior to the step of calculating an effective signal-to-interference-and-noise ratio based on the network topology model and a standard power loss propagation model, further comprising:
taking the back scattering user at the original point as a typical back scattering user, taking a gateway nearest to the typical back scattering user as a target gateway, and respectively calculating a probability density function of the distance between the typical back scattering user and the target gateway and the power received by the typical back scattering user from the power station, wherein the probability density function of the distance between the typical back scattering user and the target gateway is as follows:
where r is a typical backscatter user u 0 With target gateway g 0 A distance therebetween;
the typical backscatter user receives power from the power station as:
wherein,indicating that the power station is on subchannel c k Transmit power on, C d ＝{c k K=1, 2., where, C represents a set of orthogonal channels, P B The total transmit power of the power station on the C orthogonal channels; alpha is the path loss index of the link; />And p i -u 0 I indicates the power stations p, respectively i With typical backscatter user u 0 Small scale fading gain and distance between.
4. A method of transmission performance investigation of a large-scale wireless-powered backscatter communications network according to claim 3, wherein the step of calculating an effective signal-to-interference-and-noise ratio in accordance with the network topology model and a standard power loss propagation model further comprises:
when the power received by the typical backscatter user from the power station is greater than the power consumption of a circuit of the power station, calculating the total power received by the target gateway, wherein the total power received by the target gateway is as follows:
wherein,representing the power of the backscattered signal, < >>Representing aggregate interference I from backscattered users u ，/>Representing aggregate interference I from a power station p Beta is the backscattering coefficient of the backscattering user; phi u (c k ) To use sub-channel c k And the collected energy is sufficient to maintain a set of backscatter users of their own circuit operation; />For interfering with backscatter user u l Slave power station p i Received power; /> And->Respectively represent power stations p i With interfering backscatter user u l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 Small scale fading gains in between; ||p i -u l ||＝||X il ||、||u 0 -g 0 ||＝||R||、||u l -g 0 ||＝||X l I to I p i -g 0 ||＝||X i I indicates the power stations p, respectively i With interfering backscatter usersu l Typically backscatter user u 0 With target gateway g 0 Interference backscatter user u l With target gateway g 0 Power station p i With target gateway g 0 A distance therebetween; sigma (sigma) 2 Is the additive white gaussian noise power at the target gateway.
5. The method of claim 4, wherein the step of calculating an effective signal-to-interference-and-noise ratio based on the network topology model and a standard power loss propagation model further comprises:
and calculating the signal-to-interference-and-noise ratio of the typical backscatter user according to the total power received by the target gateway, wherein the signal-to-interference-and-noise ratio is as follows:
wherein I is p And I u Aggregate interference from power stations and backscatter users, respectively; and xi is the interference cancellation coefficient.
6. The method of claim 5, wherein the step of calculating the transmission performance of the network based on the effective signal-to-interference-and-noise ratio comprises:
obtaining the aggregate interference I from the power station according to the signal-to-interference-plus-noise ratio p And aggregate interference I from the backscattered user u Is transformed by Laplace of the aggregate interference I p Interference I with said aggregation u The laplace transform of (a) is as follows:
wherein,P at the power collected for the backscatter user is greater than the probability of circuit power consumption, i.e., the activation probability of the backscatter user.
7. The method of claim 6, wherein the step of calculating the transmission performance of the network based on the effective signal-to-interference-and-noise ratio comprises:
combining the random geometry theory according to the polymerization interference I p And the aggregate interference I u The Laplace transform of the backscattering user is obtained, and the activation probability is:
wherein,for typical backscatter user u 0 Total power collectable, +.>A mapping function for a nonlinear energy collection model; p (P) c Power consumption for the backscatter circuit; />a e And b e S is a fixed parameter related to the non-linear energy harvesting model 0 For sensitivity threshold, E max Is the maximum collectable power.
8. The method of claim 7, wherein the step of calculating the transmission performance of the network based on the effective signal-to-interference-and-noise ratio comprises:
combining the random geometric theory, and obtaining the successful transmission probability of the typical backscatter user according to the activation probability as follows:
wherein,R th and B is the transmission rate and bandwidth respectively, is->Probability density function of (a).
9. The method of claim 8, wherein the maximization model of transmission performance is as follows:
the lower bound of the optimal backscatter coefficient is:
wherein,
the sub-optimal backscattering coefficient is:
wherein,m is a parameter for balancing the complexity and the accuracy in the Gaussian Chebyshev inequality, and the value range is M more than or equal to 1.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1498382A (en) * | 2000-09-25 | 2004-05-19 | ���˹���Ѷ��� | System and method for design, tracking, measurement, prediction and optimization of data communications networks |
| EP1487139A1 (en) * | 2003-06-11 | 2004-12-15 | ARC Seibersdorf research GmbH | Channel simulator |
| CN101819609A (en) * | 2001-09-21 | 2010-09-01 | 无线谷通讯有限公司 | Be used to design, follow the tracks of, measure, predict and optimize the system and method for data communication network |
| CN110996338A (en) * | 2019-11-29 | 2020-04-10 | 河南科技大学 | Information transmission method of environment backscattering communication system with optimal energy efficiency |
| CN111556460A (en) * | 2020-04-28 | 2020-08-18 | 西安交通大学 | Power distribution method for non-ideal millimeter wave wireless power supply communication unmanned aerial vehicle network |
| CN112333761A (en) * | 2020-11-24 | 2021-02-05 | 南京工程学院 | Method for analyzing performance of ultra-density 5G cellular network based on three-dimensional random geometry |
| WO2021128608A1 (en) * | 2019-12-26 | 2021-07-01 | 重庆邮电大学 | Multi-carrier resource allocation method employing wirelessly powered backscatter communication network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7512382B2 (en) * | 2005-11-22 | 2009-03-31 | Alcatel-Lucent Usa Inc. | Modeling power amplifier and spreading code limits in a wireless system |
-
2021
- 2021-12-22 CN CN202111580229.3A patent/CN114285504B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1498382A (en) * | 2000-09-25 | 2004-05-19 | ���˹���Ѷ��� | System and method for design, tracking, measurement, prediction and optimization of data communications networks |
| CN101819609A (en) * | 2001-09-21 | 2010-09-01 | 无线谷通讯有限公司 | Be used to design, follow the tracks of, measure, predict and optimize the system and method for data communication network |
| EP1487139A1 (en) * | 2003-06-11 | 2004-12-15 | ARC Seibersdorf research GmbH | Channel simulator |
| CN110996338A (en) * | 2019-11-29 | 2020-04-10 | 河南科技大学 | Information transmission method of environment backscattering communication system with optimal energy efficiency |
| WO2021128608A1 (en) * | 2019-12-26 | 2021-07-01 | 重庆邮电大学 | Multi-carrier resource allocation method employing wirelessly powered backscatter communication network |
| CN111556460A (en) * | 2020-04-28 | 2020-08-18 | 西安交通大学 | Power distribution method for non-ideal millimeter wave wireless power supply communication unmanned aerial vehicle network |
| CN112333761A (en) * | 2020-11-24 | 2021-02-05 | 南京工程学院 | Method for analyzing performance of ultra-density 5G cellular network based on three-dimensional random geometry |
Non-Patent Citations (1)
| Title |
|---|
| 基于双重路径损耗的超密集网络性能分析;章辉;李鸾;隋学铭;吕沅宏;;天津大学学报(自然科学与工程技术版)(01);全文 * |
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