CN114641017A - Intelligent reflector assisted short packet wireless communication and energy transmission system optimization method - Google Patents
Intelligent reflector assisted short packet wireless communication and energy transmission system optimization method Download PDFInfo
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Abstract
The invention discloses an intelligent reflector assisted short packet wireless communication and energy transmission system optimization method, which comprises the steps of firstly giving statistical characteristics of signal-to-noise ratio in an ultra-reliable low-delay system supported by intelligent reflector assisted wireless energy transmission by considering that the two stages of wireless information transmission and wireless energy transmission both use short packet transmission; then, an approximate expression of the average packet error probability of the system is deduced; finally, the number of channel usages during the wireless information transfer phase and the wireless energy transfer phase is optimized to maximize the system goodput. In this system, both the transmitter and the receiver are equipped with a single antenna; the invention considers the short packet transmission scene of wireless information transmission and wireless energy transmission in the actual Internet of things system, not only carries out performance analysis on the average packet error probability of the system, but also optimizes the effective throughput of the system, and has guiding significance for engineering practice.
Description
Technical Field
The invention belongs to the technical field of ultra-reliable low-delay communication networks, and particularly relates to an intelligent reflector-assisted short-packet wireless communication and energy transmission system optimization method.
Background
With the deployment of 5G communication networks, the application of the Internet of things relates to various fields of traffic, agriculture, industry and the like, and generated data is exponentially increased. However, very large scale device connections pose significant challenges to the latency requirements and energy supply of the network. In addition, most applications of the internet of things require ultra-reliable low-latency communication. For example, the maximum communication delay tolerable by an intelligent factory varies from 250 μ s to 10ms, and the maximum tolerable error probability is 10-9. Therefore, the internet of things may require short packets with a finite length coding to reduce physical layer latency. On the other hand, internet of things devices are typically powered by batteries of limited energy supply, however, it is quite impractical to frequently replace batteries for these large-scale deployed devices. At present, a radio frequency energy collection technology becomes an effective solution for prolonging the service life of low-power-consumption internet-of-things equipment. The low power devices carrying the energy harvesting circuits may derive energy from the radio frequency signals to maintain self-operation and data transmission. Furthermore, internet of things systems supported by wireless energy transfer technologies may prefer to use short packets exclusively for energy transfer due to the inherently small data load and low latency requirements. However, as the frequency band used in future wireless communication is higher and higher, obstacles exist in reality that easily block the transmission of data or energy signals, resulting in interruption of wireless data signal transmission or wireless energy signal transmission. Therefore, due to the requirement of high reliability and low time delay in the future 5G communication system, it is very necessary to find a method for overcoming the interruption of the wireless transmission channel due to occlusion.
In recent years, the intelligent reflecting surface has received wide attention in the industry and academia, because the intelligent reflecting surface can control the wireless propagation environment by adjusting the phase or amplitude response of the self device unit, which can significantly improve the frequency spectrum and energy efficiency of the system. The intelligent reflecting surface comprises a large number of passive low-cost reflecting elements, and each device unit can independently control incident signals, and signal power enhancement or co-channel interference elimination is carried out through superposition or elimination, so that the system performance is greatly improved. Because the intelligent reflecting surface only passively reflects the incident signal, compared with the traditional active relay, the intelligent reflecting surface can save more economic cost and energy consumption, and can work in a full-duplex mode under the condition of not needing interference elimination. However, most of the current work only considers the performance analysis or resource optimization of the intelligent reflector-assisted infinite packet length data communication, but does not consider the performance analysis and optimization of the intelligent reflector-assisted high-reliability low-delay communication system supported by the wireless energy transmission technology, which is very important for the future internet of things system.
Disclosure of Invention
The invention aims to solve the problems and designs an intelligent reflector assisted short packet wireless communication and energy transmission system optimization method.
The invention achieves the above purpose through the following technical scheme:
an intelligent reflector assisted short-packet wireless communication and energy transmission system optimization method comprises the following steps.
A1, approximating the statistical characteristics of the gain of the cascade channel of the base station-intelligent reflector-equipment-intelligent reflector-base station to obtain a corresponding probability density function and a cumulative distribution function;
a2, deducing an approximate closed expression of the average packet error probability of the system;
a3, establishing a system throughput maximization problem by using the obtained packet error probability and channel use constraints in wireless information transmission and wireless energy transmission stages;
and A4, finding the optimal channel use number in the wireless information transmission and wireless energy transmission stages, and further obtaining the maximum throughput of the system.
Specifically, an expressible closed-form expression is obtained in step a2 using a linearized approximation of the Q function.
Further, in step a4, a genetic algorithm is used to quickly find a solution to the optimization problem, and the validity of the solution obtained by the genetic algorithm is verified by comparing with the exhaustive search algorithm.
The invention has the beneficial effects that:
the short packet transmission characteristic in the actual Internet of things system is considered, and the obtained system average packet error probability performance index has important guiding significance on engineering practice; in addition, based on the obtained average packet error probability and the channel use quantity constraint of the wireless information transmission stage and the wireless energy transmission stage, the effective throughput of the system close to the optimal solution can be quickly and effectively obtained by utilizing a genetic algorithm.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a system diagram of intelligent reflector assisted short packet wireless communication and energy transfer in accordance with the present invention;
FIG. 3 is a flow chart of the genetic algorithm solving process in the present invention.
In the figure: 1-a base station; 2-equipment; 3-intelligent reflecting surface.
Detailed Description
Examples of the present invention will be further described with reference to the accompanying drawings.
As shown in fig. 2, the system according to the present invention comprises a base station 1, a user equipment 2, and an intelligent reflecting surface 3 comprising N reflecting elements. In this system scenario, an "energy harvesting-information transfer" protocol is employed. In the wireless energy transmission phase, the base station 1 charges the user equipment 2 using m channel usages. During the wireless information transmission phase, the user equipment 2 transmits L information bits to the base station 1 using v channel usages using all the collected energy. The duration of one channel usage is denoted Tc。
In the embodiment, the actual rayleigh channel is considered, and the statistical distribution characteristic of the signal-to-noise ratio of the system is deduced; the short packet transmission characteristic in the actual Internet of things system is considered, and the average packet error probability performance of the system is analyzed by utilizing the linear approximation of the Q function; determining the effective throughput maximization problem of the system based on the obtained average packet error probability and the channel use constraint in the wireless information transmission and wireless energy transmission stages; finding the optimal channel use number in a wireless information transmission stage and a wireless energy transmission stage by utilizing an effective genetic algorithm, and further obtaining the maximum effective throughput of the system; and the correctness of theoretical analysis is verified by using Monte Carlo simulation, and the embodiment is shown in detail as follows.
As shown in fig. 1, a method for optimizing an intelligent reflector assisted short packet radio communication and energy transmission system specifically includes the following steps:
a1, determining the statistical characteristics of the gain of the cascade channel of the base station-intelligent reflector-equipment-intelligent reflector-base station, and obtaining the probability density function and the cumulative distribution function of the signal-to-noise ratio of the system.
The invention considers that the direct link between the base station and the equipment is blocked by some barriers and is not available, and the link can be established only through the intelligent reflecting surface. Both the base station and the user equipment are equipped with a single antenna and all channels in the system experience rayleigh fading. Thus, the complex channel vectors from the base station to the intelligent reflective surface, from the intelligent reflective surface to the user equipment, from the user equipment to the intelligent reflective surface, and from the intelligent reflective surface to the base station can be respectively represented as h1＝[h1,1,…,h1,N]H、h2＝[h2,1,…,h2,N]H、g1＝[g1,1,…,g1,N]HAnd g2＝[g2,1,…,g2,N]H. In addition, the invention assumes that the channel fading of the wireless information transmission and wireless energy transmission stages are independent and equally distributed, and therefore h1,n,h2,nCN (0, alpha) and g1,n,g2,nCN (0, beta), where alpha and beta represent fading coefficients.
In the wireless energy transmission stage, the diagonal reflection coefficient matrix of the intelligent reflection surface is expressed as
Wherein
Is the phase shift of the nth reflecting device cell. In case of ignoring intelligent countermeasuresIn the case of signal energy reflected twice or more on the surface, the total energy obtained by the user equipment during the wireless energy transmission phase can be expressed as:
where 0 < eta < 1 represents the energy conversion efficiency coefficient of the energy user, PtIs the transmit power of the base station, mTcIndicating the time the base station charges the user equipment by transmitting the short data.
In the wireless information transmission phase, the user equipment transmits L information bits to the base station using v channel usages using all collected energy. Thus, the signal received by the base station can be expressed as:
wherein x isIIs a transmitted information symbol, vTcIndicating the data transmission time, w, of sending short packetsI～CN(0,σI 2) White gaussian noise representing the information user side,
φn Ie [0,2 π). The instantaneous signal-to-noise ratio of the user equipment can then be expressed as:
wherein,
further, it is assumed that the base station knows all channel state information of the system. Therefore, in order to maximize the signal-to-noise ratio received by the user equipment and the base station, the phase shift coefficients of the components of the intelligent reflecting surface should be adjusted to meet the requirement
And
thus, the signal-to-noise ratio γ can be re-expressed as:
then, let
And derive
Distribution of (2). However,
the precise distribution is difficult to obtain, therefore, the invention utilizes moment matching technology to match
Approximated as a gamma function.
Can be expressed as a probability density function of the following standard gamma function:
wherein
The parameters of the shape are represented by,
representing the scale parameter, and Γ (·) representing the gamma function.
And (3) proving that: can know that
Can be expressed as first moment and second moment respectively
And
thus, using the known first and second moments can be separately derived
Has a mean and a variance of
And
next, by expressing the shape parameter of the gamma distribution by k and the proportion parameter of the gamma distribution by θ, a gamma distribution having the same mean and variance can be constructed, and has the following equation:
by solving the above two equations, one can obtain:
and (5) finishing the certification.
Due to the fact that
Is the sum of N independent identically distributed random variables of gamma, and thus obeys a gamma distribution with parameters Nk and θ. Next, define
Can obtain
Thus, through some mathematical transformations, the probability density function for G can be found as:
since h has been assumed1,nAnd h2,n，g1,nAnd g2,nHave the same fading coefficients alpha and beta, respectively, so that
The probability density function for Z can be expressed as:
based on the above results, the probability density function of the system signal-to-noise ratio can be expressed as:
wherein, K0Representing a second class of 0-order bessel functions.
And (3) proving that: first, ζ ═ GZ is defined. Since G and Z are statistically independent of each other, one can obtain:
finally, the cumulative distribution function of the signal-to-noise ratio can be obtained as:
wherein
K1(. cndot.) represents a second class of 1 st order Bessel functions,pFqrepresenting a generalized hypergeometric function.
And (3) proving that: the probability density function of the system signal-to-noise ratio can be obtained as follows:
it can be seen that the above-mentioned closed form expression of the system signal-to-noise ratio is difficult to obtain due to the presence of non-elementary functions. Therefore, to continue the derivation, a closed form expression of the following formula is first derived:
wherein,
is to be
Obtained after the carrying in. Specifically, replacing ω in the formula with y can obtain an expression of the system snr cumulative distribution function, and based on the series expansion of the expression when y is 0, obtain the system snr cumulative distribution function
Nk＞3,τ∈{0,1}。
A2, obtaining an expressible closed expression of the average packet error probability by utilizing the linear approximation of the Q function.
In the field of finite block coding, the average packet error probability of a quasi-static fading channel can be approximated as:
wherein,
which is indicative of the data rate, is,
which is indicative of the dispersion of the channel,
representing a gaussian Q function.
It can be seen that it is difficult to obtain a precise closed form expression of the average packet error probability due to the presence of the gaussian Q function. Thus, the Q function is first approximated as a linear function as follows:
wherein
And x0＝2Rs-1。
Based on the above results, the system average packet error probability can be expressed as:
where U (x) is expressed as a function of x,
and (3) proving that: the probability density function expression of the signal-to-noise ratio and the linearization formula of the average Q function are brought into the average packet error probability expression to obtain:
wherein S1、S2And S3The integral expressed can be obtained as proof of the cumulative distribution function of the signal-to-noise ratio.
Wherein
Wherein
Wherein
Thus, the above-mentioned S1、S2And S3Result of (1) into
The closed expression of the average packet error probability can be obtained in the formula.
A3, establishing a system throughput maximization problem by utilizing the obtained packet error probability and channel use constraints in wireless information transmission and wireless energy transmission stages;
by using the obtained expression for the packet error probability, the effective throughput of the system can be expressed as:
the invention aims to jointly optimize the channel use number in the wireless energy transmission stage and the wireless information transmission stage and maximize the effective throughput of the system. Thus, the problem of maximizing goodput for a delay constraint given the base station transmit power can be established as:
s.t.(m+v)Tc≤Ts
wherein,
representing a set of positive integers, TsRepresenting the delay of a "collect then send" process. The first constraint is a delay constraint and the second constraint indicates that the number of channel uses must be a positive integer.
And A4, obtaining the maximum throughput of the system by using a genetic algorithm according to the established optimization problem.
Through analysis, the established system throughput maximization problem is found to be a mixed integer programming problem, and is difficult to solve by using a traditional convex optimization method. The simplest method is to use an exhaustive search algorithm to obtain the system optimal solution. However, the time complexity of exhaustive search algorithms is the highest. Therefore, the method adopts the genetic algorithm to quickly solve the problem, compares the problem with the exhaustive search algorithm, and verifies the effectiveness of the algorithm. As shown in fig. 3, the genetic algorithm solving process used in the present invention is as follows:
s1, beginningInitializing population, iteration variable gen being 1, maximum number of iterations Miters40; entering a downward moving step;
s2, obtaining a use degree value through an average packet error probability formula and an established optimization problem; entering a downward moving step;
s3, judging whether the gen is satisfied or notiters(ii) a If yes, go to step S4; if not, go to step S5;
s4, outputting: optimal number of channel uses m, v, and maximum throughput
And then the process is finished;
s5, selecting operation: selecting an optimal individual by using a polling method; entering a downward moving step;
s6, crossover operation: performing a crossover operation using the point crossings; entering a downward moving step;
s7, mutation operation: using random mutation principle to produce new offspring; the process advances to step S2.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific modifications and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these modifications and combinations are within the scope of the invention.
Claims (3)
1. The intelligent reflector assisted short packet wireless communication and energy transfer system optimization method of claim 1, comprising the steps of:
a1, approximating the statistical characteristics of the gain of the cascade channel of the base station-intelligent reflector-equipment-intelligent reflector-base station to obtain a corresponding probability density function and a cumulative distribution function;
a2, deducing an approximate closed expression of the average packet error probability of the system;
a3, establishing a system throughput maximization problem by utilizing the obtained packet error probability and channel use constraints in wireless information transmission and wireless energy transmission stages;
and A4, finding the optimal channel use number in the wireless information transmission and wireless energy transmission stages, and further obtaining the maximum throughput of the system.
2. The intelligent reflector assisted short packet wireless communication and energy transfer system optimization method of claim 1, wherein: in step a2, an expressible closed-form expression is obtained using a linearized approximation of the Q function.
3. The intelligent reflector assisted short packet wireless communication and energy transfer system optimization method of claim 1, wherein: in step a4, a genetic algorithm is used to quickly find the solution of the optimization problem, and the validity of the solution obtained by the genetic algorithm is verified by comparing with the exhaustive search algorithm.
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| CN115484626B (en) * | 2022-08-09 | 2024-05-31 | 华北电力大学（保定） | RIS auxiliary short packet communication method for maximizing safety throughput |
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