LNA-based environmental backscatter communication receiver and blind symbol detection method

Abstract

This invention relates to an ambient backscatter communication receiver and a blind symbol detection method based on an LNA. The method includes constructing an ambient backscatter communication received signal model using an LNA by fusing the linear gain and nonlinear distortion of the LNA based on an ambient backscatter communication network model; designing energy detection rules based on the established received signal model, deriving an approximately optimal detection threshold that minimizes the bit error rate, and analyzing the bit error rate of the energy detector under LNA application; estimating the parameters required for the approximately optimal detection threshold based on the characteristics of the received signal samples, and calculating the estimated value of the approximately optimal symbol detection threshold based on the estimated parameters. This invention improves signal quality in low ambient RF source transmit power scenarios through LNA, and ensures detection reliability through near-optimal threshold and parameter estimation, ultimately significantly improving the symbol detection performance of the ambient backscatter communication receiver and supporting low-power, high-reliability applications of ambient backscatter communication in the Internet of Things.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an environmental backscatter communication receiver based on LNA and a blind symbol detection method. Background Technology

[0002] Ambient Backscatter Communication (AmBC) is a green wireless communication technology with enormous potential in the Internet of Things (IoT) application field. In an AmBC system, the transmitter transmits its information by modulating the incident ambient signal, rather than generating its own carrier wave, thus achieving low-power transmission. Therefore, the AmBC receiver needs to simultaneously receive both the direct ambient source signal and the backscattered signal, which presents a significant challenge to symbol detection.

[0003] Existing technologies include Energy Detection (ED) methods based on an approximately optimal detection threshold, which is obtained by estimating the information symbols transmitted by the transmitter to minimize the bit error rate (BER). Improved ED schemes extend the square operation to arbitrary positive exponentiation. Detection frameworks based on deep transfer learning utilize convolutional neural networks to extract features from the received signal. To improve detection performance, encoding techniques have also been employed at the receiver end: differential coding has been considered, a detection method based on energy difference has been proposed, and a closed-form symbol detection threshold that minimizes the BER has been derived. Other related research includes the use of non-return-to-zero coding and the proposed orthogonal space-time block coding.

[0004] Existing research assumes that the received radio frequency (RF) signal is directly down-converted to baseband and sampled before being used for energy detection (ED), and that this process does not employ a low-noise amplifier (LNA). However, in traditional point-to-point communication systems, it has been clearly established that in low signal-to-interference-plus-noise (SINR) scenarios, introducing an LNA before down-conversion can improve SINR, thereby enhancing symbol detection performance. This finding inspires the exploration of the potential benefits of integrating an LNA into an AmBC receiver. However, two key differences between traditional point-to-point communication and AmBC leave uncertainty regarding whether the application of an LNA in an AmBC receiver can improve symbol detection performance. First, in traditional point-to-point communication, only the useful signal and antenna noise at the receiver are amplified; while in AmBC, the LNA amplifies the transmitter signal (useful signal), strong direct interference, and antenna noise at both the receiver and transmitter ends simultaneously. Second, due to the difference in the signal components of the receiver in the two types of systems, the corresponding distortion signals caused by the nonlinear characteristics of the LNA are also different. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an environmental backscatter communication receiver based on LNA and a blind symbol detection method.

[0006] According to a first aspect of the present invention, an environment backscatter communication receiver based on an LNA is provided, comprising: Antenna used to receive radio frequency signals; LNA used to amplify useful signals, direct interference, and noise components; A downconversion module used for downconversion processing of RF signals amplified by LNA; A sampling module used for analog-to-digital conversion of the baseband signal after down-conversion; A threshold calculator for calculating an approximate optimal detection threshold based on the statistical characteristics of the received signal; And a decision module for making a sign determination on the sampled signal based on the energy detection rules and the threshold output by the threshold calculator.

[0007] According to a second aspect of the present invention, a method for detecting blind signals in environmental backscatter communication based on an LNA is provided, comprising the following steps: S1: Based on the environmental backscatter communication network model, the linear gain and nonlinear distortion of the LNA are integrated to construct an environmental backscatter communication signal receiving model that applies the LNA. S2: Based on the received signal model established in step S1, design the energy detection rule, derive the approximate optimal detection threshold that minimizes the bit error rate, and analyze the bit error rate of the energy detector under LNA application. S3: Estimate the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal sample from the receiver, and calculate the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters.

[0008] Based on the above scheme, the environmental backscatter communication network model includes: An environmental source used to provide the initial radio frequency signal; A transmitter used to modulate radio frequency signals received from an environmental source; And a receiver used to receive signals reflected from the transmitter and demodulate them to recover the original data information; The transmitted radio frequency signal is modulated by the transmitter via an on / off key and then reflected; all channels experience independent quasi-static Rayleigh fading. This represents the complex channel coefficients of the source-receiver link. The complex channel coefficients of the source-transmitter link are represented by... The complex channel coefficients of the transmitter-receiver link are represented by , where , and This is the path loss index. , and These represent the distances of the corresponding links; assuming the transmitter transmits data in each coherent time interval... A symbol.

[0009] Based on the above scheme, in step S1, the environmental backscatter communication signal receiving model using LNA is as follows: (2) in, The baseband equivalent signal of the receiver. For composite channel coefficients, Indicates a hypothesis , It is equivalent to additive white Gaussian noise. , , For the linear sum of LNA For the nonlinear parameters of the LNA, and The typical values ​​are respectively and .

[0010] Based on the above scheme, in step S2, the energy detection rule of the energy detector under LNA is as follows: (3) in, Is with Corresponding The total energy of a continuous sample , It is the symbol detection threshold. yes The estimated value.

[0011] Based on the above scheme, in step S2, the approximate optimal detection threshold that minimizes the bit error rate of the LNA is used. The expression is: (19) in, , .

[0012] Based on the above scheme, in step S2, the expression for the bit error rate of the receiver equipped with an LNA is: (5) in, , , , .

[0013] Based on the above scheme, step S3, which involves estimating the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal samples, and calculating the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters, specifically includes: S301: In the During the transmission of each symbol, the environmental source first uses power. Radio frequency signals are radiated towards the backscatter transmitter, which modulates its antenna impedance to transmit the binary symbols. The signal ∈{0,1} is applied to the incident signal using an on / off keyed modulation method and reflected to the receiver. The receiver simultaneously receives a weak backscattered signal from the transmitter and a strong direct interference signal from the environmental source. S302: The receiver first passes the captured RF signal through a low-noise amplifier (LNA). The LNA simultaneously amplifies the useful signal, direct interference, and antenna noise, and introduces third-order intermodulation distortion (IMD) due to its nonlinearity. The amplified signal is then down-converted to baseband and sampled to obtain the third... The pilot symbol of the first Baseband samples ;in Pilot symbol; S303: Baseband sample obtained from step S302 For each pilot symbol, calculate the corresponding... Average energy of consecutive samples The pilot symbol energy sequence is obtained; S304: Divide the pilot symbol energy sequence obtained in step S303 into two sets according to the corresponding known pilot symbol values; Group 0: Group 1: ; S305: For the grouped energy sample set obtained in step S304, calculate the mean and variance of each group of samples to obtain the parameter estimates. , , , ; S306: Substitute the parameter estimates obtained in step S305 into the optimal detection threshold formula (19) to calculate the threshold that can be used for actual detection. threshold Used for the detection and decision of subsequent data symbols to minimize the bit error rate.

[0014] Based on the above scheme, S305: For the grouped energy sample set obtained in step S304, calculate the mean and variance of each group of samples to obtain the parameter estimates. , , , Specifically, it includes: Sample mean of group 0: The sample variance of group 0: Sample mean of group 1: Sample variance of group 1: .

[0015] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention constructs a unified framework for environmental backscatter communication symbol detection integrating an LNA. By introducing linear gain coefficients and third-order nonlinear coefficients, it accurately describes the combined effects of the LNA on useful signals, direct interference, and noise, thus solving the problem of insufficient applicability of traditional models in environmental backscatter communication scenarios. First, this invention proposes a novel framework for environmental backscatter communication symbol detection integrating LNA parameters. Based on this, it derives the bit error rate (BER) for energy detection and uses the offset coefficient to prove that applying an LNA can improve symbol detection performance. Furthermore, it derives the near-optimal detection threshold that minimizes the BER and proposes a threshold parameter estimation method based on tag pilot symbols. Finally, simulation results show that under low to medium ambient source transmit power, the receiver with integrated LNA significantly reduces the BER compared to the system without an LNA. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the environmental backscatter communication system model proposed in this application; Figure 2 This is a schematic diagram of the architecture of a traditional backscatter communication receiver. Figure 3 This is a schematic diagram of the environmental backscatter communication receiver architecture equipped with an LNA proposed in this application; Figure 4 This is a graph showing the relationship between bit error rate and ambient source transmit power as proposed in this application. Figure 5 This is a graph showing the relationship between the bit error rate proposed in this application and the power ratio of the backscatter link and the direct link. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Figure 1 A model of an environmental backscatter communication system is shown, the system comprising: An environmental source used to provide the initial radio frequency signal; A transmitter used to modulate radio frequency signals received from an environmental source; And a receiver used to receive signals reflected back from the transmitter and demodulate them to recover the original data information.

[0020] Specifically, the transmitted radio frequency (RF) signal is modulated by the transmitter using on-off keying (OOK) and then reflected. All channels experience independent quasi-static Rayleigh fading. Let... This represents the complex channel coefficients of the source-receiver link. The complex channel coefficients of the source-transmitter link are represented by... The complex channel coefficients of the transmitter-receiver link are represented by , where , and This is the path loss index. , and These represent the distances of the corresponding links. This application assumes that the transmitter transmits data in each coherent time interval. A symbol.

[0021] Figure 2 The architecture of a conventional ambient backscatter communication (AmBC) receiver is shown. The radio frequency (RF) signal is first captured by the antenna, a process that introduces antenna noise. The signal is then down-converted to baseband, a process that introduces down-conversion noise. .

[0022] Since the transmitter's symbol rate is much lower than the ambient source's symbol rate, this application assumes that the transmitter's symbol rate is much lower than the ambient source's symbol rate. consecutive samples The value remains constant. Under this premise, the traditional receiver corresponds to the... The first symbol A received sample can be represented as: (1) in , Represented as a sample index, It is the number of consecutive samples corresponding to each transmitter symbol; , Indicates symbolic index, It is the total number of symbols transmitted by the transmitter within each coherent time interval; Symbol indicating a transmitter; Indicates environmental source, Indicates the emission power of the environmental source. This indicates receiver antenna noise. This indicates down-conversion noise. This indicates the transmitter antenna noise. , , , This represents the total noise at the receiver. Indicates the total noise power. It is the receiver coefficient, which represents the scattering efficiency and antenna gain.

[0023] Figure 3 An embodiment of the LNA-based environmental backscatter communication receiver of the present invention is shown, illustrating the connection relationship between the modules and the signal flow.

[0024] The LNA-based environmental backscatter communication receiver includes: Antenna used to receive radio frequency signals; LNA used to amplify useful signals, direct interference, and noise components; A downconversion module used for downconversion processing of RF signals amplified by LNA; A sampling module used for analog-to-digital conversion of the baseband signal after down-conversion; A threshold calculator for calculating an approximate optimal detection threshold based on the statistical characteristics of the received signal; And a decision module for making a sign determination on the sampled signal based on the energy detection rules and the threshold output by the threshold calculator.

[0025] The aforementioned LNA-based environmental backscatter communication receiver operates in a coordinated manner according to the signal reception → amplification → conversion → calculation → decision link: An antenna for receiving radio frequency signals captures RF signals emitted by environmental sources (containing active signals, direct interference, and noise components) and transmits them to the LNA; the LNA linearly amplifies the input RF signal (by...). Characterization) and nonlinear distortion (introducing third-order intermodulation distortion, by The amplified RF signal is output to the down-conversion module; the down-conversion module converts the amplified RF signal into a baseband signal and transmits it to the sampling module; the sampling module performs analog-to-digital conversion on the baseband signal to obtain a discrete-time sample sequence (e.g., ...). After that, it is simultaneously passed to the decision module and the threshold calculator; the threshold calculator is based on the statistical characteristics of the sample sequence (such as the mean energy under different sign assumptions). , ,variance , The threshold is calculated using the derived approximate optimal detection threshold formula (Equation 19). The data is then output to the decision module; the decision module first calculates the energy statistics for the sample sequence. Then, based on the energy detection rule (Equation 3), compare... The final output symbol estimate is obtained by applying a threshold. Each module achieves a complete processing flow from RF signal reception to symbol decision through clear signal flow and functional coordination. The application of LNA improves the signal-to-interference-plus-noise ratio, while the threshold calculator and decision module work together to complete reliable symbol detection in low-source transmit power scenarios.

[0026] In the above scheme, the receiver integrates an LNA in the RF front-end. The LNA amplifies not only the useful signal but also direct interference. and noise components Furthermore, due to the inherent nonlinearity of LNAs, intermodulation distortion (IMD) is introduced.

[0027] In this application, only odd-order nonlinear terms are considered when modeling the nonlinearity of the RF front-end, because spurious components generated by even-order terms (such as harmonics and intermodulation / intermodulation products) are assumed to fall outside the target narrowband spectrum. Furthermore, fifth-order and higher intermodulation distortions are negligible because their power is much lower than that of third-order intermodulation distortion.

[0028] Under the above assumptions, the baseband equivalent signal of the receiver It can be expressed as equation (2), (2) in For composite channel coefficients, Indicates a hypothesis , It is equivalent to additive white Gaussian noise. , , This represents the total noise at the receiver after applying an LNA. and For the linear and nonlinear parameters of the LNA, and The typical values ​​are respectively and .

[0029] An embodiment of the LNA-based blind symbol detection method for environmental backscatter communication according to the present invention.

[0030] In this embodiment, the method disclosed in this invention includes the following steps: S1: Based on the environmental backscatter communication network model, the linear gain and nonlinear distortion of the LNA are integrated to construct an environmental backscatter communication signal receiving model that applies the LNA. S2: Based on the received signal model established in step S1, design the energy detection rule, derive the approximate optimal detection threshold that minimizes the bit error rate, and analyze the bit error rate of the energy detector under LNA application. S3: Estimate the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal sample from the receiver, and calculate the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters.

[0031] This application establishes an AmBC receiver architecture model equipped with an LNA and proposes a novel AmBC symbol detection framework that integrates LNA parameters. First, considering the tag employing on-off keyed modulation combined with ED (Electronic Deposition), a closed-form expression for the Bit Error Rate (BER) and a near-optimal detection threshold that minimizes the BER are derived. Second, based on the deflection coefficient, analysis verifies that integrating an LNA can increase the probability density function difference of sampling energy when the transmitter transmits "0" and "1" symbols. Furthermore, solving for the near-optimal threshold requires prior knowledge of parameters such as channel coefficients, LNA correlation parameters, noise power, and ambient source transmit power, which are typically unknown in practical systems. To address this issue, this application proposes using the statistical characteristics of pilot signal samples to estimate the key parameters required to calculate the detection threshold.

[0032] The following provides a further explanation of the LNA-based blind symbol detection method for environmental backscatter communication in this embodiment.

[0033] S1: Based on the environmental backscatter communication network model, the linear gain and nonlinear distortion of the LNA are integrated to construct an environmental backscatter communication signal receiving model that applies the LNA. The backscatter communication signal receiving model using LNA is a complete process from signal reception → LNA processing → statistical characteristics → decision rules. Its core is incorporating the linear amplification and nonlinear distortion of the LNA into the AmBC signal model, and achieving reliable symbol recognition in low signal-to-interference-plus-noise ratio (SNR) scenarios through energy detection and near-optimal threshold design. Introducing the LNA into the AmBC system forms the basis for subsequent bit error rate analysis and parameter estimation.

[0034] Specifically, a receiver equipped with an LNA (low noise amplifier) ​​is established based on a three-node environment backscatter communication system.

[0035] The environmental backscatter communication system includes: An environmental source used to provide the initial radio frequency signal; A transmitter used to modulate radio frequency signals received from an environmental source; And a receiver used to receive signals reflected back from the transmitter and demodulate them to recover the original data information.

[0036] like Figure 3 As shown, the receiver of an environmental backscatter communication system equipped with an LNA generates a baseband equivalent signal of the following form. As shown in the following formula: (2) In the formula, It is a sequence of data symbols.

[0037] S2: Based on the received signal model established in step S1, design the energy detection rule, derive the approximate optimal detection threshold that minimizes the bit error rate, and analyze the bit error rate of the energy detector under LNA application. Specifically, S201: Using an energy detector, the transmitter symbol can be detected according to the following rules: (3) in, Is with Corresponding The total energy of a continuous sample , Indicates in the assumption Under the condition that it is established, The mathematical expectation, It is the symbol detection threshold. yes The estimated value.

[0038] S202: Approximate optimal detection threshold using a low-noise amplifier (LNA) The expression is (19) in, , . and It is an intermediate variable introduced when deriving the approximate optimal detection threshold, used to simplify the final threshold. The expression; and : respectively represent the assumptions and Down The variance.

[0039] S203: The expression for the bit error rate (BER) of a receiver equipped with a low-noise amplifier (LNA) is as follows: (5) in, , , , . express and The smaller value in express and The larger value in the range. express and The smaller value in express and The larger value in the range.

[0040] S3: Estimate the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal sample from the receiver, and calculate the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters.

[0041] Specifically, S301: in the... During the transmission of each symbol, the environmental source first uses power. Radio frequency signals are radiated towards the backscatter transmitter, which modulates its antenna impedance to transmit the binary symbols. The signal ∈{0,1} is applied to the incident signal using an on / off keyed modulation method and reflected to the receiver. The receiver simultaneously receives a weak backscattered signal from the transmitter and a strong direct interference signal from the environmental source. S302: The receiver first passes the captured RF signal through a low-noise amplifier (LNA). The LNA simultaneously amplifies the useful signal, direct interference, and antenna noise, and introduces third-order intermodulation distortion (IMD) due to its nonlinearity. The amplified signal is then down-converted to baseband and sampled to obtain the third... The pilot symbol of the first Baseband samples ;in Pilot symbol; S303: Baseband sample obtained from step S302 For each pilot symbol, calculate the corresponding... Average energy of consecutive samples The pilot symbol energy sequence is obtained; S304: Divide the pilot symbol energy sequence obtained in step S303 into two sets according to the corresponding known pilot symbol values; Group 0: Group 1: ; S305: For the grouped energy sample set obtained in step S304, calculate the mean and variance of each group of samples to obtain the parameter estimates. , , , : An estimator of the sample mean of group 0: Estimator of the sample variance for group 0: Estimators of the sample mean of group 1: Estimators of the sample variance for group 1: ; in, It is the length of the pilot symbol.

[0042] S306: Substitute the parameter estimates obtained in step S305 into the approximate optimal detection threshold formula (19) to calculate the threshold that can be used for actual detection. This threshold will be used for subsequent data symbol detection decisions to minimize the bit error rate.

[0043] (19) Based on the above embodiments and preferred embodiments, a specific embodiment is provided, which establishes an AmBC receiver architecture model equipped with an LNA and minimizes the bit error rate for energy detection, as detailed below: A. Signal model using a low-noise amplifier According to the central limit theorem, under the assumption Down, It can be approximated as a Gaussian random variable with a mean of variance is .therefore, In the assumption The probability density function (PDF) under the given conditions can be approximated as follows: (4) Theorem 1. The expression for the bit error rate (BER) of a receiver equipped with a low-noise amplifier (LNA) is: (5) in, , , , .

[0044] Proof: If The bit error rate can be expressed as (6) if The method for calculating the bit error rate is similar. (7) Combining (6) and (7) into one formula, we get (5).

[0045] Obviously yes It is a function of the expectation and variance. This leads to the derivation of these values ​​in this application, as summarized in Theorem 2.

[0046] Theorem 2. For a given and , The expected value and variance are (8) and (9), respectively. (8) (9) in .

[0047] Proof: Based on (2), It can be calculated as (10) in, , .because Based on exponential distribution The step moment formula is obtained. .

[0048] It can be deduced This leads to the following results: (11) (12) (13) The variance is expressed as: (14) (15) Based on (2), It can be calculated as (16), (16) because Established, we have (17) in , , (18) Substitute (12) and (18) into (17), then substitute the result of (13) and (17) into (15), and finally substitute (15) into (14) to obtain (9).

[0049] Using the formula given in Theorem 2 By using the mean and variance, an approximately optimal detection threshold that minimizes the bit error rate (BER) is derived. This result is summarized in Theorem 3.

[0050] Theorem 3. The approximate optimal detection threshold expression using a low-noise amplifier (LNA) is: (19) in, , .

[0051] Proof: Threshold It can be calculated using the following formula. (20) Taking the logarithm of both sides of equation (20) and solving the resulting system of equations, we can obtain equation (19).

[0052] B. Theoretical Analysis of LNA Performance Gains The offset coefficient (DC) is an important indicator for evaluating symbol detection performance because it can characterize the variance-normalized distance between the centers of two probability density functions (PDF)

[12] . Although there is no direct correlation between DC and bit error rate (BER), a higher DC value is often a reliable indicator of better BER performance. Therefore, by comparing DC values, the role of low noise amplifiers (LNAs) in improving symbol detection performance can be clearly demonstrated.

[0053] For a conventional AmBC receiver without a low-noise amplifier (LNA), its DC is given by the following formula. (twenty one) in , , .

[0054] The offset factors applied to the LNA are as follows: (twenty two) in, .

[0055] Proof: Based on equations (8) and (9), the deflection coefficient (DC) after applying a low-noise amplifier (LNA) can be expressed as equation (23). Since The value is extremely small, typically on the order of 1. dBm is even smaller, in comparison Higher-order terms can be considered negligible. Therefore, by ignoring these higher-order terms, the approximate deflection coefficients after applying LNA can be expressed as equation (22).

[0056] (twenty two) Note 1. From equations (21) and (22), it can be seen that because... Therefore Established. This application also notes that if the ambient source's emission power is sufficiently high, This will dominate the denominator of both DCs. At this point, because... and Compared to Negligible, the two coefficients converge to ,Right now The above observations indicate that the advantages of LNA are mainly reflected in the low to medium transmit power range, and the advantages diminish at high transmit power.

[0057] C. Parameter estimation required for the threshold Regarding the approximate optimal detection threshold (19) proposed in this application, the parameters need to be adjusted. and An estimation is performed. Specifically, this application estimates the energy of the transmitter pilot symbols. and Then substitute it into equation (19) to obtain the estimated threshold. The estimation method is detailed in Algorithm 1.

[0058] Algorithm 1 applies the parameter estimation method of LNA. Input: Received sample of pilot symbol Pilot symbol length , ; Output: Parameters , , , ; 1. Step 1: Calculate energy 2. For each pilot symbol ,calculate: 3. 4. Step 2: Direct Grouping 5. Divide the energy values ​​into two groups: 6. Group 0: 7. Group 1: 8. Step 3: Parameter Estimation 9. Calculate the mean and variance of each sample group: 10. 11. 12. 13. 14. Return , , , Based on the above embodiments and preferred implementations, simulation analysis was conducted. The simulation results demonstrate the advantages of the LNA-based blind symbol detection in environmental backscatter communication proposed in this invention. Unless otherwise stated, the basic parameter settings are as follows: Parameter settings are as follows: , , , m, m, m, dBm, dBm, , dB.

[0059] Figure 4 shows the bit error rate (BER) as a function of the ambient source transmit power in the case of an integrated low-noise amplifier (LNA). The detection threshold can be calculated using equation (19). As can be seen from the figure, the theoretical BER value derived in this application agrees well with the simulation results, and the accuracy of equation (19) increases with the number of samples. The error rate (BER) increases with increasing ambient source transmit power. As the ambient source transmit power increases, the BER gradually decreases; however, due to direct link interference from the ambient source, the AmBC receiver exhibits significant error rate flattening. For comparative analysis, the figure also shows results for a scenario without an LNA, demonstrating that the receiver equipped with an LNA exhibits significantly better performance than the scenario without an LNA. Nevertheless, as expected in Note 1, the performance advantage of the LNA gradually diminishes as the transmit power further increases. Figure 5 The emission power of the source in a fixed environment was plotted. The curves showing the change in BER as a function of the power ratio of the backscatter link and the direct link at dBm. The results indicate that as the backscatter link is enhanced relative to the direct link, the detection reliability is significantly improved; simultaneously, Figure 5 This further verifies the effect of integrating LNA on improving symbol detection performance.

[0060] Figure 5 This demonstrates the variation of the relative approximation error between the theoretical and estimated detection thresholds with the number of pilot symbols. The relative approximation error is defined as follows: As shown in the figure, the proposed estimated detection threshold has high accuracy, and the error decreases with the increase of the number of pilot symbols. This error reduction trend is particularly significant in the low pilot region (below 10% of the total number of symbols), while when the pilot overhead exceeds 20%, adding additional pilots can no longer significantly improve the accuracy.

[0061] The patent references the following existing literature: [1] MM Butt, NR Mangalvedhe, NK Pratas, J. Harrebek, J.Kimionis, M. Tayyab, O.-E. Barbu, R. Ratasuk, and B. Vejlgaard, "Ambient IoT: A missing link in 3GPP IoT devices landscape," IEEE Internet Things Mag., vol. 7, no. 2, pp. 85-92, 2024. [2] J. Qian, F. Gao, G. Wang, S. Jin, and H. Zhu, "Semi-coherent detection and performance analysis for ambient backscatter system," IEEETrans. Commun., vol. 65, no. 12, pp. 5266-5279, 2017. [3] S. Zargari, C. Tellambura, and A. Maaref, "Improved energy-basedsignal detection for ambient backscatter communications," IEEE Trans. Veh.Technol., vol. 73, no. 10, pp. 14 778-14 793, 2024. [4] Y. Chen and W. Feng, "Novel signal detectors for ambientbackscatter communications in internet of things applications," IEEE InternetThings J., vol. 11, no. 3, pp. 5388-5400, 2024. [5] C. Liu, Z. Wei, D. W. K. Ng, J. Yuan, and Y.-C. Liang, "Deeptransfer learning for signal detection in ambient backscattercommunications," IEEE Trans. Wireless Commun., vol. 20, no. 3, pp. 1624-1638,2021. [6] G. Wang, F. Gao, R. Fan, and C. Tellambura, "Ambient backscattercommunication systems: Detection and performance analysis," IEEE Trans.Commun., vol. 64, no. 11, pp. 4836-4846, 2016. [7] Y. Ye, J. Zhao, X. Chu, S. Sun, and G. Lu, "Symbol detection ofambient backscatter communications under IQ imbalance," IEEE Trans. Veh.Technol., vol. 72, no. 5, pp. 6862-6867, 2023. [8] S. Guruacharya, X. Lu, and E. Hossain, "Optimal non-coherentdetector for ambient backscatter communication system," IEEE Trans. Veh.Technol., vol. 69, no. 12, pp. 16 197-16 201, 2020. [9] W. Liu, S. Shen, D. H. K. Tsang, and R. Murch, "Enhancing ambientbackscatter communication utilizing coherent and non-coherent spacetimecodes," IEEE Trans. Wireless Commun., vol. 20, no. 10, pp. 6884- 6897, 2021.

[10] A. Shahed hagh ghadam, "Contributions to Analysis and DSP-BasedMitigation of Nonlinear Distortion in Radio transceivers," Ph.D.dissertation,Tampere Univ. of Technol., Tampere, Finland, 2011.

[11] Q. Zou, M. Mikhemar, and A. H. Sayed, "Digital compensation ofcross-modulation distortion in software-defined radios," IEEE J. Sel. Top.Signal Process., vol. 3, no. 3, pp. 348-361, 2009.

[12] B. Picinbono, "On deflection as a performance criterion indetection," IEEE Trans. Aerosp. Electron. Syst., vol. 31, no. 3, pp. 1072-1081, 1995.

[13] L. Shi, Y. Ye, R. Q. Hu, and H. Zhang, "System outageperformance for three-step two-way energy harvesting DF relaying," IEEETrans. Veh. Technol., vol. 68, no. 4, pp. 3600-3612, 2019. In summary, this invention constructs an environmental backscatter communication receiver model using an LNA and presents its blind symbol detection method. Specifically, firstly, a novel detection framework integrating the role of an LNA is proposed. Based on this, the bit error rate (BER) of energy detection (ED) is derived, and the near-optimal detection threshold that minimizes the BER is calculated. The performance advantages brought by introducing an LNA into the receiver are verified. Secondly, addressing the parameter estimation problem required in calculating the near-optimal detection threshold, this invention provides a practical method: using the transmitter's pilot symbols to estimate relevant parameters. Finally, simulation results fully demonstrate the performance improvement effect of integrating an LNA on symbol detection.

[0062] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. An LNA-based backscatter communication receiver, characterized by include: Antenna used to receive radio frequency signals; LNA used to amplify useful signals, direct interference, and noise components; A downconversion module used for downconversion processing of RF signals amplified by LNA; A sampling module used for analog-to-digital conversion of the baseband signal after down-conversion; A threshold calculator for calculating an approximate optimal detection threshold based on the statistical characteristics of the received signal; And a decision module for making a sign determination on the sampled signal based on the energy detection rules and the threshold output by the threshold calculator.

2. A method for blind signal detection for LNA-based ambient backscatter communication, the method comprising: The LNA-based environmental backscatter communication receiver according to claim 1 includes the following steps: S1: Based on the environmental backscatter communication network model, the linear gain and nonlinear distortion of the LNA are integrated to construct an environmental backscatter communication signal receiving model that applies the LNA. S2: Based on the received signal model established in step S1, design the energy detection rule, derive the approximate optimal detection threshold that minimizes the bit error rate, and analyze the bit error rate of the energy detector under LNA application. S3: Estimate the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal sample from the receiver, and calculate the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters.

3. The LNA-based ambient backscatter communication blind signal detection method of claim 2, wherein, The environmental backscatter communication network model includes: An environmental source used to provide the initial radio frequency signal; A transmitter used to modulate radio frequency signals received from an environmental source; And a receiver used to receive signals reflected from the transmitter and demodulate them to recover the original data information; The transmitted radio frequency signals are reflected after being modulated by the on-off keying of the transmitters, and all channels experience independent quasi-static Rayleigh fading; let denote the complex channel coefficients of the environment source-receiver link, denote the complex channel coefficients of the environment source-transmitter link and denote the complex channel coefficients of the transmitter-receiver link, where , and are the path loss exponents, , and denote the distances of the corresponding links, respectively. It is assumed that the transmitter transmits symbols in each coherence time interval.

4. The LNA-based ambient backscatter communication blind signal detection method according to claim 2, wherein, In step S1, the environmental backscatter communication signal receiving model using LNA is as follows: (2) wherein is the baseband equivalent signal of the receiver, is the composite channel coefficient, denotes the assumption , is the equivalent additive white Gaussian noise, , , is the linear and is the non-linear parameter of the LNA, and typical values of and are 0.5 and 0.1, respectively.

5. The LNA-based ambient backscatter communication blind signal detection method of claim 2, wherein, In step S2, the energy detection rule of the energy detector under LNA is as follows: (3) wherein is the total energy of the corresponding to consecutive samples, , is a symbol detection threshold, is an estimate of .

6. The method for detecting blind signals in environmental backscatter communication based on LNA according to claim 2, characterized in that, The step S2 uses the minimum bit error rate approximation optimal detection threshold value of LNA The expression is: (19) wherein , .

7. The LNA-based ambient backscatter communication blind signal detection method of claim 2, wherein, In step S2, the expression for the bit error rate of the receiver equipped with an LNA is: (5) wherein , , , .

8. The LNA-based ambient backscatter communication blind signal detection method of claim 6, wherein, S3: The step of estimating the parameters required for the approximate optimal detection threshold in step S2 based on the characteristics of the received signal samples, and calculating the estimated value of the approximate optimal symbol detection threshold based on the estimated parameters, specifically includes: S301: In the During the next symbol transmission, the environmental source first uses power Radio frequency signals are radiated towards the backscatter transmitter, which modulates its antenna impedance to transmit the binary symbols. The signal ∈{0,1} is applied to the incident signal in an on / off keying manner and reflected to the receiver. The receiver simultaneously receives a weak backscattered signal from the transmitter and a strong direct interference signal from the environmental source. S302: The receiver first passes the captured RF signal through a low-noise amplifier (LNA). The LNA simultaneously amplifies the useful signal, direct interference, and antenna noise, and introduces third-order intermodulation distortion (IMD) due to its nonlinearity. The amplified signal is then down-converted to baseband and sampled to obtain the third... The pilot symbol of the first Baseband samples ;in Pilot symbol; S303: Baseband sample obtained from step S302 For each pilot symbol, calculate the corresponding... Average energy of consecutive samples The pilot symbol energy sequence is obtained; S304: Divide the pilot symbol energy sequence obtained in step S303 into two sets according to the corresponding known pilot symbol values; Group 0: Group 1: ; S305: For the grouped energy sample set obtained in step S304, calculate the mean and variance of each group of samples to obtain the parameter estimates. , , , ; S306: Substitute the parameter estimates obtained in step S305 into the optimal detection threshold formula (19) to calculate the threshold that can be used for actual detection. threshold Used for the detection and decision of subsequent data symbols to minimize the bit error rate.

9. The method for detecting blind signals in environmental backscatter communication based on LNA according to claim 8, characterized in that, S305: For the grouped energy sample set obtained in step S304, calculate the mean and variance of each group of samples to obtain the parameter estimates. , , , Specifically, it includes: Sample mean of group 0: ; The sample variance of group 0: ; Sample mean of group 1: ; Sample variance of group 1: .

Images