CN108092926A - The parameter estimation algorithm of passive backscatter communication channel - Google Patents
The parameter estimation algorithm of passive backscatter communication channel Download PDFInfo
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Abstract
The present invention relates to a kind of parameter estimation algorithms of passive backscatter communication channel, solves total blindness's estimation problem in the system using passive backscatter communication technology, it has handled the observation data occurred in total blindness's estimation procedure and has contained multiple hidden variables and channel parameter to be estimated with equipment reflective condition without the problem of replacing variation of breaking off a friendship, and rationally effective algorithm iteration initial value is devised, obtains good estimation performance.The key point of the present invention is：For multiple hidden variables, the associated situation of multiple channel parameters, by introducing variable Sj, θmIt handles, and derives the closed solution of iterative process intermediate variable.Setting for iterative initial value need not obtain additional information, and give specific value range.It is to be protected point：(1) for more hidden variables, the processing method of multi-parameter inversion and corresponding theory deduction result in backscatter technique.(2) setting method and specific value range of the backscatter technique on iterative initial value.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a parameter estimation algorithm of a passive backscatter communication channel.
Background
The passive backscattering technology is a new thing, can enable a sensor to get rid of the constraint of a battery, avoids frequent manual maintenance operation, has the characteristics of wireless convenience and low energy consumption of green communication, has a wide application prospect in related products of an RFID (radio frequency identification) system and the Internet of things industry, and is a wireless technology with commercial value and potential. Passive backscatter technologies include ambient backscatter technologies, wiFi backscatter technologies, and FM backscatter technologies, among others, which can use energy harvesting means to obtain the energy needed for sensor operation from the wireless signals already in the vicinity. The passive backscattering technology can be applied to the field of information transmission and forms a communication technology different from the traditional backscattering, namely the passive backscattering communication technology. The design of basic modules such as channel estimation and signal detection in the passive backscatter communication technology is different from that of other backscatter communication technologies, the theoretical result of the existing traditional backscatter communication technology cannot be directly applied to the passive backscatter communication technology, the related communication theory is not improved, and the important item is the channel estimation theory. Existing signal detection techniques for passive backscatter communications are based on the assumption that the channel is perfectly known, or based on the assumption that the received signal is statistically characterized, thereby avoiding channel estimation, e.g., energy detection, differential detection [4] . Actually, the accurate channel estimation can reduce the error rate of detection and improve the detection efficiency of the system.
There is a united states patent entitled "estimation-based channel estimation and signal detection for wireless communications systems" (channel estimation and signal detection based on Expectation-maximization algorithm in wireless communications systems) [1] And corresponding thesis [2] . The technology is mainly aimed at an OFDM communication system, frequency response estimation and impulse response estimation are respectively carried out on a convolution channel of OFDM by using an EM algorithm, and for EMIn the initial value setting problem of the iterative process, the technology carries out rough estimation by utilizing the channel historical information of the previous frame or sending a pilot frequency sequence, the estimation is non-blind estimation, the hidden variable in the algorithm is a single variable, and the parameter to be estimated is in a convolution matrix form. This technique has the following disadvantages:
1. the technique is not suitable for the total blind estimation, and the setting method of the initial value has poor practicability, and the system needs to acquire additional information. If a certain system utilizes a passive backscattering communication technology, backscattering equipment is passive equipment, energy required by the backscattering equipment is obtained by a wireless acquisition module, and power obtained by energy conversion is insufficient to support the passive equipment to send a pilot signal.
2. The hidden variables involved in this technique are single variables. In some wireless communication systems, if a full blind estimation is used, there may be more than one hidden variable in addition to the parameters to be estimated, such as in a system utilizing passive backscatter communication techniques, there may be a source signal and a reflected signal that are not a single hidden variable, and the reflected signal in the hidden variables is associated with the channel parameters to be estimated.
3. The parameter to be estimated in the technology is a convolution matrix, is mainly suitable for OFDM scenes, and is not suitable for other communication systems which cannot be converted into the same form. Such as systems utilizing passive backscatter communications techniques, the channel parameters are constantly changing with both the reflective and non-reflective states of the passive device.
There is a paper entitled "prediction-Maximization-Based Channel Estimation for Multi-user MIMO Systems" (EM-Based Channel Estimation algorithm for multi-user multiple input multiple output Systems) [3] . The technology mainly aims at the MIMO communication system, estimates the channel between multiple users and the base station by using an EM algorithm, and solves the problem of setting an initial value in an EM iterative process. This technique requires the receiving end and the transmitting end to communicate with each other in the earlier stage to obtain the channel estimationAnd calculating necessary information of the process, and then carrying out accurate estimation through an EM algorithm, wherein each user independently estimates a channel, and the parameter to be estimated is in a vector form. This technique has the following disadvantages:
1. the technique is not suitable for the full-blind estimation, and the acquisition method of the initial value is very complicated. The method is not suitable for a scene of blind estimation and cannot be realized in some wireless communication systems, for example, a system using a passive backscatter communication technology, in which a backscatter device is a passive device, energy of the backscatter device is acquired by wireless acquisition, and converted power can only support two states of reflection and non-reflection, and complex functions such as signal transceiving cannot be realized.
2. In the technology, each user carries out channel estimation respectively, the form of the parameter to be estimated is a vector, and although a plurality of channel parameters are provided, the channels are independent from each other, namely, the channel parameters are equivalent to single parameter estimation of a plurality of channels. For other communication systems which cannot convert channels into vector form, such as systems using passive backscatter communication technology, the channel parameters change continuously with two states of reflection and non-reflection of passive devices, and the system model is essentially single-channel multi-parameter estimation.
Existing channel estimation techniques are not applicable to systems that utilize passive backscatter communications techniques. The passive devices need to utilize the information source assistance of other peripheral wireless signals to complete the communication. Because the signals of the peripheral wireless signal sources are random and unknown, the channel estimation technology needs to solve the problem of channel blind estimation unknown to the signal sources, except the peripheral wireless signal sources, the reflection state of the equipment is also unknown, and the observation data contains non-single hidden variables. And the channel parameters to be estimated are changed alternately along with the reflection state of the equipment, the channel estimation technology needs to estimate two channel parameters simultaneously, and a reasonable estimation initial value is designed according to the known information.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a parameter estimation algorithm of a passive backscatter communication channel, solve the problem of total-blind estimation in a system utilizing a passive backscatter communication technology, solve the problems that observation data in the total-blind estimation process contains a plurality of hidden variables and channel parameters to be estimated are continuously and alternately changed along with the reflection state of equipment, design a reasonable and effective algorithm iteration initial value and obtain good estimation performance.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a system utilizing passive backscatter communications techniques comprising: an RF signal source, a reader and a tag; the RF signal source, the reader-writer and the tag are in wireless communication, and the reader-writer and the tag are in wireless communication; the tag communicates with the reader/writer by using a wireless signal of a peripheral existing RF signal source, wherein the wireless signal of the RF signal source may be a wireless broadcast signal, a radio signal, or the like.
A parameter estimation algorithm for a passive backscatter communications channel, comprising the steps of:
s1, defining a channel parameter between an RF signal source and a reader-writer as h, a channel parameter between the reader-writer and a label as g, and a channel parameter between the label and the RF signal source as f; wherein the channel parameter g is a constant, the channel parameter h and the channel parameter f are both slow fading channels, and obey the gaussian distribution of zero mean value respectively: h to N (0,N) h )，f～N(0，N f ) In which N is h Representing the variance, N, of the channel parameter h f Represents the variance of the channel parameter f;
step S2, defining BPSK signal sent by RF signal source as x (n), x (n) has two values, respectivelyWherein P is s Is the transmit power of the RF signal source; defining B (n) as binary information transmitted by the tag, wherein B (n) =1 when the tag reflects and B (n) =0 when the tag does not reflect; the received signal y (n) at the receiving end of the reader-writer is represented as:
where w (N) is noise, follows a zero mean Gaussian distribution, and has a variance of N w η represents the attenuation coefficient of x (n) inside the tag;
when B (n) =0, the label keeps silent, and the reader-writer can estimate the modulus of the channel parameter h according to the single-parameter single-hidden-variable EM algorithm
When B (N) =1, the composite channel parameter between the tag and the reader-writer is mu, zero mean value obeys, and variance is N μ Gaussian distribution of (a): mu to N (0,N) μ ) In which N is μ ＝N h +σ 2 N f μ is expressed as:
μ＝h+σf (2)
wherein σ = η g;
then, the received signal y (n) at the receiving end of the reader-writer is simplified as follows:
y(n)＝θ m x(n)+w(n),m＝1,2 (3)
wherein theta is m Representing two channel parameters h and mu of the label in different reflection states;
step S3, defining an iteration counter n =0, and receiving the modulus value and the transmission power P of the signal y (n) through the receiving end of the reader-writer s Coarse estimation of channel parameters theta 1 、θ 2 In turn, in accordance with the channel parameter θ 1 、θ 2 Determining an iteration initial value;
the following variable q is first defined:
the form of the initial value of the iteration is as follows:
θ 1 (0) ＝q-ε,0<ε<q (12)
θ 2 (0) ＝q+ε,0<ε<q (13)
wherein e represents the increment of the initial value on q;
under the condition of low signal-to-noise ratio, directly applying formulas (12) and (13) to set an iteration initial value; when the signal-to-noise ratio is increased, the influence of noise is planed out by reducing the range of epsilon, and when the transmission power P is increased s When the following conditions are satisfied:
and (3) reducing the value range of the epsilon to the following range:
step S4, introducing two variables S j And Q j (i) Analyzing two hidden variables x (n) and B (n) existing in a signal y (n) received by a receiving end of a reader-writer, wherein S j Represents a pairwise combination of two hidden variable values, j =1,2,3,4, q j (i) Represents S in the case of a known received signal y (i) j The probability of occurrence;
s5, deducing Q according to EM algorithm j (i) The expression of (c) is specifically as follows:
wherein, f (y (i) | S i ；θ m ) Is shown at known S i In the case of (b), the probability density function of y (i), p (S) i ) Denotes each kind of S i Probability of occurrence, x k Represents a transmitted signal at a certain time N, k = a or b, e represents the base of the natural logarithm, i =1,2, … N;
the same can be obtained:
s6, calculating the lower bound of the likelihood function of the received signal y (n) at the receiving end of the reader-writer by using the Jesen inequality
Next, the channel parameter θ of the label in different reflection states 1 、θ 2 Viewed as the lower bound of the likelihood functionRespectively to the channel parameter theta 1 、θ 2 And solving the partial derivative to obtain a channel parameter value of the next iteration process, wherein the channel parameter value is as follows:
then determining a lower bound of the likelihood functionWhether convergence is achieved or not and whether convergence conditions are satisfied or notWherein tau is any constant greater than 0 and represents the precision of the iterative algorithm; if the convergence condition is not met, updating an iteration counter, wherein n = n +1, and repeating the steps S5-S6; if the convergence condition is satisfied, obtaining an estimated value of the channel parameter:
step S7, calculatingAnd withAnd finally judging that: and modulus valueThe closer estimate is the estimate of channel parameter h and the other is the estimate of channel parameter mu.
On the basis of the above scheme, in step S4, S j The correspondence between the two hidden variables x (n) and B (n) is specifically:
when j =1,m =1, the value of x (n) is x a ；
When j =2,m =1, the value of x (n) is x b ；
When j =3,m =2, the value of x (n) is x a ；
When j =4,m =2, the value of x (n) is x b 。
Drawings
The invention has the following drawings:
FIG. 1 is a system model diagram.
Figure 2 mean square error versus blind estimate lower bound.
Figure 3 is a plot of mean square error versus signal length.
Fig. 4 is a flow chart of a parameter estimation algorithm for a passive backscatter communications channel in accordance with the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1-4, the system model to which the present invention is directed is shown in fig. 1, and the communication system is composed of three parts: an RF signal source, a reader-writer and a label. Unlike the conventional RFID system, the tag communicates with the reader/writer by using the radio frequency signal of the existing RF signal source, for example, a radio broadcast signal, a radio signal, etc. can be used as the RF signal source in the system. The communication principle is as follows: the tag represents two states, 0 and 1, by reflecting and not reflecting the wireless signal, wherein the two states, reflecting and not reflecting, are realized by the tag by changing the impedance of the antenna.
Suppose that the channel parameter between the RF signal source and the reader/writer is h, the channel parameter between the reader/writer and the tag is g, and the channel parameter between the tag and the signal source is f. And assume that parameters h and f are both slow fading channels and obey a zero-mean gaussian distribution (also known as a normal distribution). I.e., h to N (0,N) h )，f～N(0，N f ). Wherein N is h ，N f Representing the variance of the corresponding channel parameter. Since the distance between the reader/writer and the tag is much smaller than the distance between the reader/writer and the RF source and the distance between the tag and the RF source, it is assumed that the channel parameter g between the reader/writer and the tag is constant. Suppose that the BPSK signal transmitted by the RF signal source is x (n), and the two values are respectivelySuppose B (n) is binary information conveyed by a tag, a tag reflection represents a 1 and a non-reflection represents a 0.η represents the attenuation coefficient of x (n) inside the tag. The received signal y (n) at the receiving end of the reader-writer can be represented as:
where w (n) is noise, obeying a zero mean Gaussian distribution,variance of N w 。
When B (n) =0, the label keeps silent, and the reader-writer can estimate the modulus of the channel parameter h according to the single-parameter single-hidden-variable EM algorithm
μ is defined as a composite channel parameter between the tag and the reader/writer when B (n) = 1:
μ＝h+σf (2)
where σ = η g. Thus, the composite channel parameter μ also follows a zero mean with a variance of N μ Gaussian distribution of (i.e., μ to N) (0,N) μ ) In which N is μ ＝N h +σ 2 N f . The received signal at the receiving end of the reader-writer can be simplified as follows:
y(n)＝θ m x(n)+w(n),m＝1,2 (3)
wherein theta is m Representing two channel parameters h and mu for different reflection states of the tag.
The blind estimation algorithm of the channel aims at simultaneously estimating the modulus values of two channel parameters h and mu by the receiving end of the reader according to the received signal y (n). The principle is as follows:
for the selection of the iteration initial value, the invention adopts the following mode: defining an iteration counter n =0, and roughly estimating two parameters theta through a module value and a transmission power of a received signal 1 、θ 2 Determining an iteration initial value according to the range of the true value, and defining the following variable q:
the initial iteration value selected by the invention is in the form of:
θ 1 (0) ＝q-ε,0<ε<q (12)
θ 2 (0) ＝q+ε,0<ε<q (13)
wherein e represents the increment of the initial value on q;
in the case of a low snr, the estimation performance is inevitably affected by noise, and the initial values may be selected in the form of (12) and (13). When the signal-to-noise ratio is increased, the influence of noise can be planed out by reducing the range of epsilon to obtain more stable performance when the transmission power P is increased s When the following conditions are satisfied:
the range of values of e can be reduced to the following range:
because two hidden variables x (n) and B (n) exist in a received signal y (n) and each hidden variable has two values, two variables S are introduced j ，Q j (i) To help resolve this:
(1)S j represents four cases of two-by-two combination of two hidden variable values, wherein j =1,2,3,4.
(2)Q j (i) Represents S in the case of a known received signal y (i) j The probability of occurrence.
Wherein S is j The correspondence to the two hidden variables is shown in table 1:
| S j | θ m | x(n) |
| j＝1 | m＝1 | x a |
| j＝2 | m＝1 | x b |
| j＝3 | m＝2 | x a |
| j＝4 | m＝2 | x b |
Q j (i) The expression of (c) can be derived from the principle of EM algorithm,
wherein, f (y (i) | S i ；θ m ) Is shown at known S i In the case of y (i), the probability density function of θ m Is an unknown channel parameter; p (S) i ) Denotes each kind of S i Probability of possible occurrence, x k Represents a transmitted signal at a certain time N, k = a or b, e represents the base of the natural logarithm, i =1,2, … N; . The same can be obtained:
next, using the Jesen inequality, a lower bound for the received signal likelihood function may be calculated
Two channel parameters theta of different reflection states of the label 1 、θ 2 Viewed as the lower bound of the likelihood functionRespectively to the channel parameter theta 1 、θ 2 And solving the partial derivatives to obtain the following parameter values in the next iteration process:
then determining a lower bound of the likelihood functionWhether to converge or not and whether to satisfy the convergence conditionWherein tau is any constant greater than 0 and represents the precision of the iterative algorithm; if the convergence condition is not met, updating an iteration counter, wherein n = n +1, and repeating the steps S5-S6; if the convergence condition is satisfied, obtaining an estimated value of the channel parameter:
final calculationAndand finally judging that: and modulus valueThe closer estimate is the estimate of channel parameter h and the other is the estimate of channel parameter μ.
In summary, the key technical means of the invention is to introduce the variable S j ，θ m Processing a plurality of hidden variables and the situations of two channel parameters, and deducing to obtain closed solutions of formulas (4) - (10); secondly, a feasible value range of the iteration initial value is given, extra information is not needed, the two parameters are distinguished in the iterative calculation process, and good estimation performance is achieved, as shown in fig. 2 and 3. The MBCRB is an improved Bayesian-Cramer lower bound, is mainly applied to the condition that the parameter to be estimated is a random variable of known prior information, and the observation data contains other unknown parameter information, and is slightly smaller than the real lower bound. Fig. 4 shows a flow chart of the algorithm of the present invention.
The channel estimation algorithm of the present invention is specifically summarized as follows:
1. the receiving end of the reader-writer determines an iteration initial value according to the results of the formulas (11) - (15), and then calculates S according to the formulas (4) - (7) j Probability Q of each possible occurrence j (i) And calculating a lower bound of a likelihood function of the received signalI.e. step E in the EM algorithm.
2. According to Q in the previous step j (i) As a result of (2), derivation bounds the lower likelihood functionAnd (4) maximizing, and obtaining parameter values (9) - (10) of the next iteration, namely M steps in the EM algorithm.
3. Repeating the above two steps until the lower bound of the likelihood functionAnd (6) converging.
4. When the label keeps silent, the reader-writer can estimate the module value of the parameter h according to the EM algorithm of the single-parameter single-hidden variableAnd then calculateAndthe estimated value closer to the h-mode value is finally judged to be the estimated value of the channel parameter h, and the other estimated value is the estimated value of the channel parameter mu.
When the blind estimation method of the channel parameters is applied to channel estimation of a system using a passive backscatter communication technology, the change curve of MSE (mean Square error) along with SNR (signal to noise ratio) is shown in FIG. 2, and when the signal length N =10 and the SNR =20dB, the MSE can reach 10 -2 Order of magnitude, with good estimation performance. Fig. 3 is an image of the variation of the MSE with the signal length, and it can be seen that, in the process of increasing the signal length from 2 to 10, the curve attenuation degree is high, the MSE gradually decreases, and after the signal length exceeds 12, the curve tends to be flat, that is, the present invention can approximately obtain the best effect under the condition of short signal length.
The key points of the invention are as follows: (1) For the case that a plurality of hidden variables and a plurality of channel parameters are associated, a variable S is introduced j ，θ m To process and derive a closed solution for the intermediate variable of the iterative process. (2) For the setting of the iteration initial value, no extra information needs to be acquired, and a definite value range is given.
Points to be protected: (1) A processing method for multi-hidden variable and multi-parameter estimation in the backscattering technology and a corresponding theoretical derivation result are disclosed. (2) The backscattering technology relates to a setting method and a specific value range of an iteration initial value.
Accessories:
references (e.g. patents/papers/standards)
[1]Ma Xiaoqiang,Kobayashi Hisashi,Schwartz Stuart C.,Gu Daqing,and Zhang Jinyun,“Expectation-maximization-based channel estimation and signal detection for wireless communications systems”,U.S.Patent 7092436,August 15,2006.
[2]Xiaoqiang Ma,Hisashi Kobayashi,and Stuart C.Schwartz.2004.“EM-based channel estimation algorithms for OFDM”,EURASIP J.Appl.Signal Process.2004(January 2004),1460-1477.
[3]S.Park,J.W.Choi,J.Y.Seol and B.Shim,"Expectation-Maximization-Based Channel Estimation for Multiuser MIMO Systems,"in IEEE Transactions on Communications,vol.65,no.6,pp.2397-2410,June 2017.
[4]G.Wang,F.Gao,R.Fan and C.Tellambura,"Ambient Backscatter Communication Systems:Detection and Performance Analysis,"in IEEE Transactions on Communications,vol.64,no.11,pp.4836-4846,Nov.2016.
Those not described in detail in this specification are within the skill of the art.
Claims (2)
1. A parameter estimation algorithm for a passive backscatter communications channel, wherein a system utilizing passive backscatter communications techniques comprises: an RF signal source, a reader and a tag; the RF signal source, the reader-writer and the tag are in wireless communication, and the reader-writer and the tag are in wireless communication; the tag communicates with the reader-writer by using a wireless signal of a peripheral existing RF signal source, wherein the wireless signal of the RF signal source comprises: wireless broadcast signals, radio signals;
the method comprises the following steps:
s1, defining a channel parameter between an RF signal source and a reader-writer as h, a channel parameter between the reader-writer and a label as g, and a channel parameter between the label and the RF signal source as f; wherein the channel parameter g is a constant, the channel parameter h and the channel parameter f are both slow fading channels, and obey the gaussian distribution of zero mean value respectively: h to N (0,N) h )，f～N(0，N f ) In which N is h Representing the variance, N, of the channel parameter h f Represents the variance of the channel parameter f;
step S2, defining BPSK signal sent by RF signal source as x (n), x (n) has two values, respectivelyWherein P is s A transmit power of an RF signal source; defining B (n) as binary information transmitted by the tag, wherein B (n) =1 when the tag reflects and B (n) =0 when the tag does not reflect; the received signal y (n) at the receiving end of the reader-writer is represented as:
where w (N) is noise, follows a zero mean Gaussian distribution, and has a variance of N w η represents the attenuation coefficient of x (n) inside the tag;
when B (n) =0, the label keeps silent, and the reader-writer can estimate the modulus of the channel parameter h according to the single-parameter single-hidden-variable EM algorithm
When B (N) =1, the composite channel parameter between the tag and the reader is mu, obeying zero mean value, and the variance is N μ Gaussian distribution of (a): mu to N (0,N) μ ) In which N is μ ＝Nh+σ 2 N f μ is expressed as:
μ＝h+σf (2)
wherein σ = η g;
then the received signal y (n) at the receiving end of the reader-writer is simplified as follows:
y(n)＝θ m x(n)+w(n),m＝1,2 (3)
wherein theta is m Representing two channel parameters h and mu of the label in different reflection states;
step S3, defining an iteration counter n =0, and receiving the modulus value and the transmission power P of the signal y (n) through the receiving end of the reader-writer s Coarse estimation of channel parameters theta 1 、θ 2 In turn, in accordance with the channel parameter θ 1 、θ 2 Determining an iteration initial value;
the following variable q is first defined:
the form of the initial value of the iteration is as follows:
θ 1 (0) ＝q-ε,0<ε<q (12)
θ 2 (0) ＝q+ε,0<ε<q (13)
wherein e represents the increment of the initial value on q;
under the condition of low signal-to-noise ratio, directly applying formulas (12) and (13) to set an iteration initial value; when the signal-to-noise ratio is increased, the influence of noise is planed out by reducing the range of epsilon, and when the transmission power P is increased s When the following conditions are satisfied:
and (3) reducing the value range of the epsilon to the following range:
step S4, introducing two variables S j And Q j (i) Analyzing two hidden variables x (n) and B (n) existing in a signal y (n) received by a receiving end of a reader-writer, wherein S j Represents a pairwise combination of two hidden variable values, j =1,2,3,4, q j (i) Substitute for Chinese traditional medicineTable in the case of a known received signal y (i), S j The probability of occurrence;
s5, deducing Q according to EM algorithm j (i) The expression of (c) is specifically as follows:
wherein, f (y (i) | S i ；θ m ) Is shown at known S i In the case of (b), the probability density function of y (i), p (S) i ) Denotes each kind of S i Probability of occurrence, x k Represents a transmitted signal at a certain time N, k = a or b, e represents the base of the natural logarithm, i =1,2, … N;
the same can be obtained:
s6, calculating the lower bound of the likelihood function of the received signal y (n) at the receiving end of the reader-writer by using the Jesen inequality
Next, the channel parameter θ of the tag in different reflection states 1 、θ 2 Viewed as the lower bound of the likelihood functionRespectively to the channel parameter theta 1 、θ 2 And solving the partial derivative to obtain a channel parameter value of the next iteration process, wherein the channel parameter value is as follows:
then determining a lower bound of the likelihood functionWhether convergence is achieved or not and whether convergence conditions are satisfied or notWherein tau is any constant greater than 0 and represents the precision of the iterative algorithm; if the convergence condition is not met, updating an iteration counter, wherein n = n +1, and repeating the steps S5-S6; if the convergence condition is satisfied, obtaining an estimated value of the channel parameter:
step S7, calculatingAnd withAnd finally judging that: and modulus valueThe closer estimate is the estimate of channel parameter h and the other is the estimate of channel parameter mu.
2. The parameter estimation algorithm for a passive backscatter communications channel of claim 1 wherein in step S4, S j The correspondence between the two hidden variables x (n) and B (n) is specifically:
when j =1,m =1, the value of x (n) is x a ；
When j =2,m =1, the value of x (n) is x b ；
When j =3,m =2, the value of x (n) is x a ；
When j =4,m =2, x (n) takes a value of x b 。
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