Cellular passive internet of things backscattering adjacent frequency interference suppression demodulation method and device

Abstract

The embodiment of the application discloses a backscattering communication adjacent frequency interference suppression demodulation method and device for cellular passive Internet of Things, and relates to the technical field of wireless communication. The method comprises the following steps: acquiring an OFDM signal, respectively performing signal processing and deconvolution on the OFDM signal to obtain a modulation signal and a sending signal, and training a neural network to obtain a reconstruction model; receiving a time-domain OFDM signal, reconstructing the time-domain OFDM signal through the reconstruction model, and obtaining an adjacent band interference signal generated by time-domain OFDM signal leakage through a band-pass filter; respectively performing Fourier transformation on the time-domain OFDM signal and the adjacent band interference signal, performing maximum ratio combining after interference elimination, and then performing maximum likelihood detection to obtain original information corresponding to the time-domain OFDM signal. The neural network reconstruction model is constructed to effectively process the OFDM signal, high-precision reconstruction of the signal is realized, accurate identification and elimination of adjacent band interference are realized, and the problem that the prior art cannot suppress interference, resulting in high bit error rate of the communication system and low communication quality is solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for suppressing demodulation of backscattered adjacent frequency interference in a cellular passive Internet of Things. Background Art

[0002] Cellular passive IoT backscatter (BBS) communication technology plays a key role in 5G-A and 6G communications. Cellular communication systems boast efficient spectrum utilization and wide coverage, making them suitable for the deployment of passive IoT devices. Passive BBS utilizes signals from cellular base stations for data transmission, making it suitable for large-scale IoT applications. Therefore, the present invention integrates passive IoT BBS devices with cellular communication systems to provide a more efficient, low-energy IoT communication solution for smart devices and applications. Cellular passive IoT BBS specifically adjusts the impedance of the BBS device to reflect the excitation source signal, providing a low-power solution for IoT and sensor networks. It also enhances signal coverage and supports large-scale connections, opening up possibilities for emerging application areas such as smart cities and intelligent manufacturing. As a key enabling technology for future communication systems, BBS technology is expected to promote the realization of the "Internet of Everything" and inject new impetus into the development of 5G-A and 6G networks.

[0003] A cellular passive IoT backscatter (BBS) communication system involves a cellular base station transmitting a sinusoidal excitation carrier to a BBS device. The BBS device then harvests a portion of the received signal to maintain normal operation. It then uses the excitation signal to adjust its impedance for reflection, transmitting the BBS information back to the base station. RFID technology is a typical example of this technology. This technology frees tags from battery constraints, solving the energy challenges of IoT sensors and playing a crucial role in the future of the IoT.

[0004] However, backscatter (backscatter) communication under broadband adjacent-band interference uses the guard band frequency band to transmit data, which will be affected by the adjacent-band interference signal leaked from the broadband signal. Moreover, since the backscatter device has no power amplifier and the reflected signal undergoes two fading cycles, its power is much smaller than the signal power of the adjacent-band interference leaked by the uplink user. The signal strength difference can be as high as 20-30dB, which greatly affects the accurate reception and demodulation of the signal.

[0005] Therefore, there is an urgent need for a cellular passive Internet of Things backscatter adjacent frequency interference suppression demodulation method that can achieve low bit error rate, high efficiency and anti-interference based on the backscatter communication characteristics. Summary of the Invention

[0006] The various embodiments of the present invention provide a method for suppressing backscattered adjacent-band interference in a cellular passive Internet of Things (IoT) system. This method addresses the problem that existing technologies, when faced with complex nonlinear distortion and strong adjacent-band interference, cannot achieve ideal interference suppression, resulting in high bit error rates and low communication quality in the communication system. The technical solution is as follows:

[0007] According to one aspect of the present invention, a cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation method comprises: acquiring an OFDM signal, performing signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, and using the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model; receiving a time-domain OFDM signal, reconstructing the time-domain OFDM signal through the reconstruction model, and obtaining an adjacent-band interference signal generated by leakage of the time-domain OFDM signal through a bandpass filter; performing Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, respectively, to obtain multiple interference-eliminated signals; performing maximum ratio combining on the multiple interference-eliminated signals and then performing maximum likelihood detection to obtain the original information corresponding to the time-domain OFDM signal.

[0008] In one embodiment, the OFDM signal is processed and deconvolved to obtain a modulated signal and a transmitted signal respectively by the following steps: removing the cyclic prefix, performing fast Fourier transform and decision detection on the OFDM signal to obtain frequency domain symbols, performing IFFT and adding a cyclic prefix on the frequency domain symbols to obtain a modulated signal; estimating the communication channel to obtain channel parameters, and deconvolving the OFDM signal according to the channel parameters to obtain a transmitted signal.

[0009] In one embodiment, the use of the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model is achieved by the following steps: using the modulated signal as input and the transmitted signal as output to train the neural network, and continuously adjusting internal parameters of the neural network during the training process; obtaining a loss value based on the difference between the output of the neural network and the OFDM signal, and ending the training when the loss value reaches a set threshold to obtain a reconstruction model.

[0010] In one embodiment, the reconstruction model includes an input layer, 5 fully connected layers, an activation function and an output layer, the fully connected layer includes 30 neurons, each of the fully connected layers is connected to one activation function, and the output layer includes 2 neurons.

[0011] In one embodiment, the input layer includes the envelope term of the input signal, higher-order terms of the envelope term, and various-order delay terms.

[0012] In one embodiment, performing maximum ratio combining on the multiple interference-eliminated signals and then performing maximum likelihood detection to obtain the original information corresponding to the time-domain OFDM signal is achieved by the following steps: performing maximum ratio combining on the multiple interference-eliminated signals to obtain multiple enhanced signals, and performing maximum likelihood detection on the multiple enhanced signals to obtain the original information corresponding to the time-domain OFDM signal;

[0013] The calculation formula of the maximum likelihood detection is:

[0014] Wherein, x(l) represents the enhanced signal, y(l) represents the time domain OFDM signal, and L represents the length of the signal.

[0015] In one embodiment, the maximum likelihood detection further comprises:

[0016] According to the maximum likelihood decision criterion The decision maker is calculated:

[0017] Wherein, N represents the number of receiving antennas, H1 is the assumption that b(l)=1, and H0 is the assumption that b(l)=0.

[0018] In one embodiment, the probability distribution of x(l) is

[0019] In one embodiment, the method further includes the following steps: if the backscattered signal is deployed on the resource blocks at the edges of both sides of the spectrum, the reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signal on one side; if the backscattered signal is deployed on the middle resource blocks of the spectrum, the reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signals on both sides respectively.

[0020] According to one aspect of the present invention, a cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation device comprises: a neural network training module for acquiring an OFDM signal, performing signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, and using the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model; a signal reconstruction module for receiving a time-domain OFDM signal, reconstructing the time-domain OFDM signal through the reconstruction model, and obtaining an adjacent-band interference signal generated by leakage of the time-domain OFDM signal through a bandpass filter; an interference elimination module for performing Fourier transform elimination of interference on the time-domain OFDM signal and the adjacent-band interference signal to obtain multiple interference-eliminated signals; and a signal demodulation module for performing maximum ratio combining on the multiple interference-eliminated signals and then performing maximum likelihood detection to obtain original information corresponding to the time-domain OFDM signal.

[0021] The beneficial effects brought about by the technical solution provided by the present invention are:

[0022] In the above technical solution, the present invention first obtains an OFDM signal, performs signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, uses the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model, receives a time-domain OFDM signal, reconstructs the time-domain OFDM signal through the reconstruction model, obtains an adjacent-band interference signal generated by leakage of the time-domain OFDM signal through a bandpass filter, performs Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, obtains a plurality of interference-eliminated signals, performs maximum ratio combining on the plurality of interference-eliminated signals, and then performs maximum likelihood detection to obtain the original information corresponding to the time-domain OFDM signal, and constructs a neural network. The network reconstruction model effectively processes OFDM signals, achieves high-precision reconstruction of signals and accurate identification and elimination of adjacent-band interference, separates interference signals through bandpass filters, and combines frequency domain processing technology to accurately eliminate interference in the frequency domain, significantly improving signal quality. Utilizes maximum ratio combining and maximum likelihood detection technology to recover original information from multiple interference-eliminated signals, improving the accuracy and reliability of information transmission, and providing strong support for the stable communication of OFDM systems in high-interference environments. This can effectively solve the problem that existing technologies cannot achieve ideal interference suppression effects when faced with complex nonlinear distortion and strong adjacent-band interference, resulting in high bit error rates and low communication quality in communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0024] Figure 1 This is a flow chart of a method for suppressing and demodulating backscattered adjacent-channel interference in a cellular passive Internet of Things according to an exemplary embodiment;

[0025] Figure 2 It is a schematic diagram of the implementation environment of a backscatter communication system application scenario in an application scenario;

[0026] Figure 3 yes Figure 2 Schematic diagram of signal deployment of backscatter communication system in corresponding application scenarios;

[0027] Figure 4 yes Figure 2 Schematic diagram of the structure of the reflection device in the corresponding application scenario;

[0028] Figure 5 yes Figure 2 Flowchart of the demodulation method for suppressing backscattered adjacent-channel interference in cellular passive IoT in the corresponding application scenario;

[0029] Figure 6 yes Figure 2 Schematic diagram of the demodulation method for suppressing backscattered adjacent-channel interference in cellular passive IoT in the corresponding application scenario;

[0030] Figure 7 yes Figure 2 Schematic diagram of the structure of the neural network in the corresponding application scenario;

[0031] Figure 8 yes Figure 2 Comparison diagram of OFDM signals in the embodiment and the prior art in corresponding application scenarios;

[0032] Figure 9 yes Figure 2 The effect diagram of eliminating interference signals in the frequency domain in the corresponding application scenario;

[0033] Figure 10 yes Figure 2 Comparison of bit error rates of backscatter communication systems under adjacent-channel interference in corresponding application scenarios;

[0034] Figure 11 The present invention is a block diagram of a cellular passive Internet of Things backscattered adjacent channel interference suppression and demodulation device according to an exemplary embodiment. DETAILED DESCRIPTION

[0035] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0036] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present disclosure refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0037] The present invention provides a cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation method. By constructing a neural network reconstruction model to effectively process OFDM signals, the method achieves high-precision reconstruction of the signal and accurate identification and elimination of adjacent-band interference, solving the problem that the existing technology cannot achieve the ideal interference suppression effect, resulting in a high bit error rate and low communication quality in the communication system. The cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation method is suitable for a cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation device. The cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation device can be an electronic device. The cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation method in the embodiment of the present invention can be applied to a variety of scenarios, such as cellular passive Internet of Things backscattered adjacent-band interference suppression and demodulation.

[0038] See also Figure 1 , an embodiment of the present invention provides a cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation method, which is applicable to electronic equipment.

[0039] In the following method embodiments, for ease of description, the execution subject of each step of the method is taken as an electronic device as an example for illustration, but this does not constitute a specific limitation.

[0040] like Figure 1 As shown, the method may include the following steps:

[0041] Step 110: Obtain an OFDM signal, perform signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, and use the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model.

[0042] In one possible implementation, the OFDM signal is subjected to cyclic prefix removal, fast Fourier transform, and decision detection to obtain frequency domain symbols, the frequency domain symbols are subjected to IFFT and cyclic prefix addition to obtain a modulated signal, the communication channel is estimated to obtain channel parameters, and the OFDM signal is deconvolved based on the channel parameters to obtain a transmitted signal.

[0043] In one possible implementation, the modulated signal is used as input and the transmitted signal is used as output to train the neural network. During the training process, the neural network continuously adjusts its internal parameters, and a loss value is obtained based on the difference between the output of the neural network and the OFDM signal. When the loss value reaches a set threshold, the training ends and a reconstructed model is obtained.

[0044] In one possible implementation, the reconstruction model includes an input layer, 5 fully connected layers, an activation function, and an output layer. The fully connected layer includes 30 neurons, each fully connected layer is connected to an activation function, and the output layer includes 2 neurons.

[0045] In one possible implementation, the input layer includes the envelope term of the input signal, higher-order terms of the envelope term, and various-order delay terms.

[0046] Step 130 : Receive a time-domain OFDM signal, reconstruct the time-domain OFDM signal using a reconstruction model, and obtain an adjacent-band interference signal generated by leakage of the time-domain OFDM signal using a bandpass filter.

[0047] Step 150: Perform Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, thereby obtaining a plurality of interference-eliminated signals.

[0048] Step 170: Perform maximum ratio combining on the multiple interference-eliminated signals and then perform maximum likelihood detection to obtain original information corresponding to the time-domain OFDM signal.

[0049] In one possible implementation, maximum ratio combining is performed on multiple interference-eliminated signals to obtain multiple enhanced signals, and maximum likelihood detection is performed on the multiple enhanced signals to obtain original information corresponding to the time-domain OFDM signal.

[0050] Specifically, the calculation formula of maximum likelihood detection is:

[0051]

[0052] Wherein, x(l) represents the enhanced signal, y(l) represents the time domain OFDM signal, and L represents the length of the signal.

[0053] In one possible implementation, maximum likelihood detection further includes:

[0054] According to the maximum likelihood decision criterion The decision maker is calculated:

[0055]

[0056] Wherein, N represents the number of receiving antennas, H1 is the assumption that b(l)=1, and H0 is the assumption that b(l)=0.

[0057] In one possible implementation, the probability distribution of x(l) is

[0058] In one possible implementation, if the backscattered signal is deployed on the resource blocks at the edges of the spectrum, a reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signal on one side; if the backscattered signal is deployed on the middle resource blocks of the spectrum, a reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signals on both sides.

[0059] Through the above process, the present invention first obtains the OFDM signal, performs signal processing and deconvolution on the OFDM signal respectively to obtain the modulation signal and the transmission signal, uses the modulation signal and the transmission signal to train the neural network to obtain the reconstruction model, receives the time domain OFDM signal, reconstructs the time domain OFDM signal through the reconstruction model, obtains the adjacent band interference signal generated by the leakage of the time domain OFDM signal through a bandpass filter, performs Fourier transform on the time domain OFDM signal and the adjacent band interference signal respectively to eliminate interference, obtains multiple signals after eliminating interference, performs maximum ratio combining on the multiple signals after eliminating interference, and then performs maximum likelihood detection to obtain the original information corresponding to the time domain OFDM signal, and constructs a neural network. The network reconstruction model effectively processes OFDM signals, achieves high-precision reconstruction of signals and accurate identification and elimination of adjacent-band interference. It separates interference signals through bandpass filters and combines frequency domain processing technology to accurately eliminate interference in the frequency domain, significantly improving signal quality. It uses maximum ratio combining and maximum likelihood detection technology to recover the original information from multiple interference-eliminated signals, improving the accuracy and reliability of information transmission, and providing strong support for the stable communication of OFDM systems in high-interference environments. It can effectively solve the problem that existing technologies cannot achieve ideal interference suppression effects when faced with complex nonlinear distortion and strong adjacent-band interference, resulting in high bit error rate and low communication quality in the communication system.

[0060] In one application scenario, Figure 2 A schematic diagram of the implementation environment of a backscatter communication system application scenario is shown. Figure 3 The signal deployment diagram of the backscatter communication system in this application scenario is shown. Figure 4 The schematic diagram of the reflective device is shown. Figure 5 The flowchart of the demodulation method for suppressing backscattered adjacent-channel interference in cellular passive IoT is presented. Figure 6 A schematic diagram showing the demodulation method for suppressing backscattered adjacent-channel interference in cellular passive IoT is shown. Figure 7 The schematic diagram of the neural network structure is shown. Figure 8 A comparison diagram of OFDM signals in accordance with the present invention and prior art is shown. Figure 9 The effect of eliminating interference signals in the frequency domain is shown. Figure 10 A comparison chart of the bit error rates of backscatter communication systems under adjacent channel interference is shown.

[0061] like Figure 2 As shown, the backscatter communication system includes an access point (such as an RFID system reader or an access point of other types of backscatter communication systems) and a reflection device, and considers the influence of out-of-band interference generated by broadband cellular signals of adjacent frequency users.

[0062] like Figure 3As shown, the backscattered uplink signal occupies resource block n, and the uplink cellular signal is transmitted in the adjacent frequency band. The out-of-band interference signal generated by the nonlinear distortion of the power amplifier will seriously affect the accurate reception of the backscattered signal.

[0063] like Figure 4 As shown, each reflection device includes: a backscatter antenna module: used to receive and reflect signals from environmental access points; a backscatter modulation module: changes the load impedance of the antenna according to the information symbol to achieve backscatter modulation; a microcontroller module: used to control the communication process of the reflection device; a signal processor module: used for basic signal processing of the reflection device, such as decoding of control signals; a RF energy harvester and battery module: used to collect energy from the incident signal and charge the battery to power all modules; other modules, including storage, sensing, clock and other units.

[0064] The reflection device modulates the incident signal it receives by switching the load impedance to change the amplitude and / or phase of its backscattered signal, and the backscattered signal is received and eventually decoded by the full-duplex access point.

[0065] like Figure 5 As shown, the cellular passive Internet of Things backscatter adjacent frequency interference suppression demodulation method may include the following steps:

[0066] Step S1: The base station sends a downlink excitation signal.

[0067] In step S2, the reflecting device collects energy and backscatters the received signal according to the information it carries.

[0068] In step S3, the base station receives the broadband cellular signal and generates training data to train the deep neural network.

[0069] In step S4, the base station receives and generates out-of-band interference generated by the broadband cellular signal of the adjacent frequency user, and eliminates the interference in the frequency domain.

[0070] Step S5: performing maximum ratio combining and maximum likelihood detection on the signal obtained by eliminating interference in sequence to achieve signal demodulation.

[0071] The signal in step S1 is a pure carrier signal deployed on resource block n. In step S2, the power reflection coefficient of the reflector is configured as a fixed constant known to the access point. The reflector absorbs the signal transmitted by the access point and obtains energy therefrom. The reflector reflects the incident signal back to the access point with different amplitudes and phases by continuously changing the antenna impedance, thereby modulating the incident signal and achieving communication with the access point.

[0072] Furthermore, because the base station's downlink excitation signal is deployed in resource block n, the backscatter device transmits reflected signals in the frequency bands of these resource blocks. However, spectrum leakage from adjacent-band wideband cellular signals due to nonlinear distortion in the power amplifier can cause strong adjacent-band interference signals to enter these resource blocks. The difference in strength between the leaked wideband interference signal and the reflected signal can be as high as 20-30dB, severely impacting demodulation performance.

[0073] In step S3, the reflection device uses the broadband cellular signal generated by demodulation and remodulation at the receiving end before and after being affected by the nonlinear distortion of the power amplifier to train a deep neural network to fit the nonlinear behavior of the power amplifier at the transmitting end, and is used for the subsequent interference reconstruction method.

[0074] In step S4, the base station generates a time-domain broadband cellular signal after performing judgment detection on the received signal, and uses the neural network trained in step S3 to reconstruct the time-domain cellular signal sent by the user end. It further obtains the leaked adjacent-band interference signal through filtering, and performs Fourier transform on the time-domain signal received by the antenna and the reconstructed adjacent-band interference signal respectively to eliminate interference in the frequency domain.

[0075] It is worth mentioning that in step S2, the backscattered uplink signal occupies resource block n, and its adjacent resource blocks all transmit uplink broadband cellular signals.

[0076] An OFDM signal consists of a superposition of multiple orthogonal subcarriers, each carrying a portion of data. When multiple subcarriers are in phase, they can form a large peak, resulting in a high peak-to-average power ratio (PAPR) for the OFDM signal. A high PAPR means that the signal's peak power is much higher than the average power, which easily causes the power amplifier to enter a nonlinear operating region. Especially when the power amplifier attempts to amplify these high peaks, the nonlinear effect not only affects the signal's original frequency components but also generates new harmonics and intermodulation products in the spectrum. These new frequency components may appear outside the original OFDM band, resulting in out-of-band emissions. This out-of-band emission can interfere with adjacent channels, significantly impacting other communication systems, especially in spectrum-crowded environments.

[0077] In addition, since the backscatter device has no power amplifier and the reflected signal undergoes two round-trip fading, its power is much smaller than the signal power of the adjacent-band interference leaked by the uplink user, so an efficient interference cancellation algorithm is required at the receiving end.

[0078] like Figure 6As shown in the figure, during the training phase of the neural network, on the one hand, the base station sequentially removes the cyclic prefix, performs FFT, and performs decision detection on the OFDM signal it receives to obtain the originally transmitted frequency domain symbols, and then performs I FFT and adds the cyclic prefix to obtain the originally transmitted time domain OFDM modulated signal as the input of the neural network; on the other hand, the base station deconvolves the received OFDM signal with the estimated channel to obtain the time domain OFDM signal sent by the user-end antenna as the output of the neural network to remove interference from other factors and ensure accurate fitting of the nonlinear distortion of the power amplifier.

[0079] Furthermore, in step S4, when the backscatter signal is deployed in the resource blocks on both sides, a neural network needs to be used to reconstruct and eliminate the interference of the cellular signal leakage on one side. When the backscatter signal is deployed in the middle resource block, it is only necessary to reconstruct and eliminate the interference of the cellular signal leakage on both sides.

[0080] The resource block occupancy is set as follows: the backscatter communication uplink occupies RB0 and RB1 resource blocks, and the corresponding baseband frequency range is f0~f1; the OFDM signal occupies RB2~RB N The center frequency of the resource block RB0 and RB1 is f I =(f0+f1) / 2, so it is necessary to shift the OFDM signal after the PA fitted by the neural network to the left in the frequency domain by f I , then design the cutoff frequency to be f c =(f1-f0) / 2 to obtain the equivalent baseband signal of its leakage interference, and then move it to the f0~f1 frequency band in the frequency domain to obtain the interference y of the OFDM signal leaked in the backscatter link frequency band I (l), namely:

[0081]

[0082] Among them, y I [l] is the out-of-band interference of the OFDM signal after the nonlinear PA reconstructed at the receiving end. The interference simulated by the receiving end after passing through the channel can be expressed as:

[0083]

[0084] in, The channel response of the uplink cellular signal estimated by the receiving end.

[0085] The signal received by the receiving end of the backscatter communication system can be expressed as: y(l) = y u (l)+y i (l)+z(l),y u (l) represents the useful signal containing the modulation information of the reflecting device, y i(l) is the out-of-band interference leaked from the adjacent band cellular user, z(l) is the power Additive Gaussian noise, The receiving end first uses the results of neural network fitting Eliminate out-of-band interference signals in the frequency domain, and then perform operations such as channel estimation and equalization, decoding and decision making.

[0086] Finally, a simulation experiment is used to verify the interference reconstruction and elimination effect of the present invention. Without loss of generality, the simulation verification here uses a memory polynomial baseband model to simulate the nonlinear effect of the power amplifier (PA) on the OFDM signal:

[0087]

[0088] like Figure 7 As shown, in order to improve the accuracy of modeling the nonlinearity of the power amplifier, the network model used in the simulation here is an enhanced real-valued time-delay neural network, which has 20 feature inputs and 2 feature outputs. The model uses the two orthogonal components of the input signal I in and Q in To predict the two components I corresponding to the output signal out and Q out In addition to the real and imaginary parts of the signal, the network input also improves the nonlinear processing capability of the model by adding the envelope term of the input signal and the higher-order terms of the envelope term, and simulates the memory effect of the power amplifier by adding delay terms of various orders.

[0089] The network's internal structure consists of five fully connected layers, each containing 30 neurons. Each linear layer is followed by a Leaky ReLU activation layer. The characteristic of a Leaky ReLU is that it allows small negative gradients to pass through. That is, when the input value is negative, the output is a small proportion of the negative input (here, 0.01). This avoids the "dead neuron" problem of the ReLU activation function, that is, some neurons output only zero values ​​during training.

[0090] like Figure 8As shown in the figure, a comparison of OFDM signals before and after the power amplifier and a deep neural network modeling performance graph are shown. Among them, the black line represents the power spectral density of the OFDM signal that is not affected by the nonlinearity of the power amplifier. At around 10MHz, the signal power of its sidelobe leakage will rapidly decay from -80dBm to -130 to -140dBm; the red line represents the power spectral density of the OFDM signal after the PA nonlinear distortion. At around 10MHz, the signal power of its sidelobe leakage will only decay from -80dBm to -90 to -100dBm, which can be seen that it has caused serious out-of-band interference; and the blue line represents the nonlinear distortion effect fitted by using neural network modeling. It can be seen that the two have a high degree of overlap, indicating that the neural network can fit the out-of-band distortion part very well.

[0091] Define the normalized mean square error (NMSE) calculation formula:

[0092]

[0093] Among them, y i represents the actual observed value, The NMSE represents the predicted value. It measures the error between the model's predicted value and the actual observed value. Its normalization makes the result independent of the data scale, making it suitable for comparison across different datasets. The calculated NMSE between the OFDM distorted signal fitted by the neural network and the actual value is -40 dB, demonstrating that the deep neural network is able to effectively account for the effects of PA nonlinear distortion.

[0094] like Figure 9 As shown in the figure, the effect of eliminating interference signals in the frequency domain at the receiving end of the backscattering system is shown, where the black cross solid line is the power spectrum of the signal received at the receiving end, which includes the adjacent-band interference leaked from the OFDM signal output by the nonlinear power amplifier, the label backscattered signal and noise; the black asterisk solid line is the power spectrum of the signal after the adjacent-band interference leaked from the OFDM signal output by the nonlinear power amplifier reconstructed by the neural network and the adjacent-band interference is eliminated in the frequency domain; the black circle solid line is the power spectrum of the background noise.

[0095] Depend on Figure 9 It can be seen intuitively that due to the influence of leakage interference from adjacent-band users, the peak power spectral density of the received signal is about 45dBm / Hz higher than the noise floor. After the neural network reconstructs the interference leakage from adjacent-band users and eliminates this interference in the frequency domain, the peak power spectral density of the received signal can be reduced by about 45dBm / Hz. At this time, the received signal after interference elimination is on par with the noise floor power, indicating that the interference is almost completely eliminated.

[0096] Furthermore, the Interference Rejection Combining (IRC) technique is used as a comparison method for comparison with the embodiments of the present invention. The core of IRC technology is to leverage the receiver's multi-antenna system to effectively distinguish and suppress interfering signals while preserving useful signal components. This is achieved by applying specific weights to the received signal vectors. These weights are designed to maximize the desired signal-to-interference-and-noise ratio (SINR).

[0097] Assuming the received signal is y(n), it can be expressed as:

[0098] y(n)=Hs(n)+H i s i (n)+n,

[0099] According to the principle of IRC algorithm, the transmitted signal using maximum likelihood estimation is:

[0100]

[0101] in, is the target signal estimate, H is the channel response matrix, Q is the interference noise covariance matrix, H H It represents the deconjugated transpose of the matrix. For the traditional IRC algorithm, to achieve the effect of interference suppression, the estimation of the covariance matrix of the interference noise and the channel response is the key.

[0102] like Figure 10 As shown in the figure, a comparison chart of the bit error rate of the backscatter communication system under adjacent channel interference is shown, wherein the black solid line is the reference bit error rate performance without interference and with known channel response, the blue solid line is the bit error rate performance of the IRC method, and the red solid line is the bit error rate performance of the neural network interference reconstruction and elimination method of the present invention.

[0103] Depend on Figure 9 It can be seen that under given conditions, the bit error rate performance of the embodiment of the present invention is significantly better than that of the IRC method, and at a bit error rate of , the performance of the neural network method is improved by about 6dB compared with the IRC method, and the performance is degraded by about 2.5dB compared with the ideal case of known channels and no interference.

[0104] Through the above process, the embodiment of the present invention realizes efficient processing of adjacent-channel interference in the cellular passive Internet of Things backscatter communication system through the broadband cellular signal adjacent-channel interference reconstruction and elimination technology based on deep neural networks, which can significantly reduce the impact of adjacent-channel interference on the backscatter signal and improve the signal reception quality. By accurately reconstructing and eliminating adjacent-channel interference, this method significantly reduces the demodulation bit error rate of the backscatter information. Compared with the traditional interference rejection combining (IRC) technology, it has better performance at the same bit error rate, indicating that it can effectively improve the accuracy of signal demodulation.

[0105] In another application scenario, a backscatter communication system includes a full-duplex base station and a reflection device. The full-duplex base station is equipped with N+1 receiving antennas, one of which is used to transmit a single-tone excitation signal deployed in the cellular system's guard band and receive the backscattered signal. The backscatter device switches different impedances according to the information bits to achieve different backscattering coefficients for backscattering. The N antennas of the full-duplex base station are used for cellular system communications. However, backscatter communication detection deployed in the guard band is susceptible to interference from adjacent-band leakage of uplink broadband signals. The present invention designs a method for adjacent-band interference reconstruction and elimination based on deep learning, and proposes a corresponding signal model and signal detection algorithm to achieve low-bit-error-rate backscatter information demodulation.

[0106] Specifically, the backscatter communication reflected signal is deployed on the resource block n (RB n ), since the base station will not only receive the reflected signal y u , and will also receive strong interference signals y from the uplink broadband signal leaking into the guard band i , assuming that the lth sampling point of the base station sending the single tone signal is s(l), and the lth sampling point of the reflection device's own signal is

[0107] Where p is the transmit power, b(l) is the Manchester coded BPSK modulation symbol, and f c is the frequency at which the impedance of the reflecting device switches, T s is the sampling frequency. The base station not only receives the reflected signal y u , and receives the strong interference signal y from the adjacent band leakage of the uplink cellular terminal broadband signal i , so the baseband signal received by the base station is:

[0108]

[0109] Among them, g b The downlink channel for the base station to send a single tone signal to the backscatter device, g f For the uplink channel between the reflection device and the base station receiver, is the phase noise, ⊙ is the Hadamard product, y u (l) is the effective signal received from the reflection device, y i (l) is adjacent band interference, For background noise.

[0110] Among them, the backscatter device is equipped with an antenna for signal reflection. The base station sends a downlink single-tone signal. The reflection device selects different backscatter coefficients for backscattering according to the information bits. The base station reconstructs and eliminates interference and detects the signal of the reflection device.

[0111] Specifically, the base station first sends a downlink excitation signal in the form of a pure carrier signal deployed on resource block n. The reflection device then collects energy and adjusts the impedance based on the information it carries to backscatter the received signal. The power reflection coefficient is a fixed constant. The device receives the excitation signal transmitted by the base station and obtains energy from it. By changing the antenna impedance, the device reflects the signal back to the access point with different amplitudes and phases to achieve signal modulation and communication with the base station. Since the base station downlink excitation signal is deployed on resource block 0 and resource block 1, the backscatter device reflected signal is also located in the frequency band of the resource block. In addition, the adjacent frequency broadband cellular signal will generate leaked adjacent band interference, and the nonlinear distortion of the power amplifier will significantly increase the power of the leaked adjacent band interference signal.

[0112] Furthermore, to reconstruct the leaked adjacent-band interference signal, using a neural network at the base station to reconstruct and eliminate interference requires first training the neural network. During the training phase, the base station removes the cyclic prefix, performs FFT, and performs decision detection on the received OFDM signal to obtain the originally transmitted frequency-domain symbols. This is then processed through an I FFT and adds a cyclic prefix to obtain the originally transmitted time-domain OFDM modulated signal as the neural network input. Furthermore, the base station deconvolves the received OFDM signal with the estimated channel, obtaining the time-domain OFDM signal transmitted by the user-end antenna as the neural network output.

[0113] Furthermore, in the actual deployment phase, since the nonlinear behavior of the power amplifier is mainly determined by its physical and electrical characteristics, such as the characteristics of the transistor and the design of the amplifier, these characteristics usually do not change significantly in a short period of time. Therefore, this trained model can be used to model the actual received signal in subsequent communications. The base station generates a time domain OFDM signal after performing judgment detection on the received signal, and uses the generated neural network to reconstruct the time domain OFDM signal sent by the user end. The adjacent band interference signal generated by the leakage of the time domain OFDM signal is obtained through a bandpass filter. The time domain signal received by the antenna and the reconstructed adjacent band interference signal are Fourier transformed respectively to eliminate interference in the frequency domain.

[0114] Furthermore, the signals obtained by eliminating interference are sequentially subjected to maximum ratio combining and maximum likelihood detection to achieve signal demodulation.

[0115] Specifically, the background noise in maximum likelihood detection obeys the complex Gaussian distribution, let

[0116] Maximum likelihood decision criterion The decision maker is calculated:

[0117] in, For the assumption that b(l)=1, Assuming b(l) = 0, the probability distribution of x(l) is:

[0118]

[0119] Through the above process, the present invention effectively processes OFDM signals by constructing a neural network reconstruction model, realizes high-precision reconstruction of signals and accurate identification and elimination of adjacent-band interference, separates interference signals through bandpass filters, and accurately eliminates interference in the frequency domain in combination with frequency domain processing technology, significantly improving signal quality, and utilizes maximum ratio combining and maximum likelihood detection technology to recover original information from multiple interference-eliminated signals, thereby improving the accuracy and reliability of information transmission, and providing strong support for the stable communication of OFDM systems in high-interference environments, thereby effectively solving the problem that existing technologies cannot achieve ideal interference suppression effects when facing complex nonlinear distortion and strong adjacent-band interference, resulting in high bit error rate and low communication quality of the communication system.

[0120] The following are device embodiments of the present invention, which can be used to implement the cellular passive Internet of Things backscatter adjacent channel interference suppression and demodulation method involved in the present invention. For details not disclosed in the device embodiments of the present invention, please refer to the method embodiments of the cellular passive Internet of Things backscatter adjacent channel interference suppression and demodulation method involved in the present invention.

[0121] See also Figure 11 In an embodiment of the present invention, a cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation device 800 is provided.

[0122] The cellular passive Internet of Things backscattered adjacent channel interference suppression and demodulation device 800 includes but is not limited to: a neural network training module 810, a signal reconstruction module 830, an interference elimination module 850 and a signal demodulation module 870.

[0123] Among them, the neural network training module 810 is used to obtain the OFDM signal, perform signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, and use the modulated signal and the transmitted signal to train the neural network to obtain a reconstruction model.

[0124] The signal reconstruction module 830 is configured to receive a time-domain OFDM signal, reconstruct the time-domain OFDM signal using a reconstruction model, and obtain an adjacent-band interference signal generated by leakage of the time-domain OFDM signal using a bandpass filter.

[0125] The interference cancellation module 850 is configured to perform Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to cancel interference, thereby obtaining a plurality of interference-cancelled signals.

[0126] The signal demodulation module 870 is configured to perform maximum likelihood detection after maximum ratio combining of the multiple interference-eliminated signals to obtain original information corresponding to the time-domain OFDM signal.

[0127] It should be noted that the cellular passive Internet of Things backscattered adjacent channel interference suppression and demodulation provided in the above embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the cellular passive Internet of Things backscattered adjacent channel interference suppression and demodulation device will be divided into different functional modules to complete all or part of the functions described above.

[0128] In addition, the cellular passive Internet of Things backscatter adjacent channel interference suppression and demodulation device provided in the above embodiments and the cellular passive Internet of Things backscatter adjacent channel interference suppression and demodulation method embodiments belong to the same concept, and the specific manner in which each module performs operations has been described in detail in the method embodiments and will not be repeated here.

[0129] Compared with the related art, the present invention has the following beneficial effects:

[0130] 1. The present invention first obtains an OFDM signal, performs signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, uses the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model, receives a time-domain OFDM signal, reconstructs the time-domain OFDM signal through the reconstruction model, obtains an adjacent-band interference signal generated by leakage of the time-domain OFDM signal through a bandpass filter, performs Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, obtains multiple signals after eliminating interference, performs maximum ratio combining on the multiple signals after eliminating interference, and then performs maximum likelihood detection to obtain the original information corresponding to the time-domain OFDM signal, and constructs a neural network. The reconstruction model effectively processes OFDM signals, achieves high-precision reconstruction of signals and accurate identification and elimination of adjacent-band interference, separates interference signals through bandpass filters, and accurately eliminates interference in the frequency domain in combination with frequency domain processing technology, significantly improving signal quality. Utilizing maximum ratio combining and maximum likelihood detection technology, the original information is recovered from multiple interference-eliminated signals, improving the accuracy and reliability of information transmission, and providing strong support for the stable communication of OFDM systems in high-interference environments. This can effectively solve the problem that existing technologies cannot achieve ideal interference suppression effects when faced with complex nonlinear distortion and strong adjacent-band interference, resulting in high bit error rates and low communication quality in communication systems.

[0131] 2. This invention is highly adaptable: By using a neural network to model the nonlinear distortion of power amplifiers, it can accommodate power amplifiers with varying physical and electrical characteristics. Because these characteristics typically do not change significantly over short periods of time, a trained neural network model can be effectively applied in practical communications over the long term.

[0132] 3. This invention improves communication efficiency: By optimizing the signal processing process, including cyclic prefix removal, FFT, decision detection, IFFT, and cyclic prefix addition, this method improves communication efficiency while maintaining communication quality. This contributes to more efficient data transmission in IoT devices and sensor networks.

[0133] 4. This invention supports large-scale connections: Backscatter technology, a key enabling technology for future communication systems, offers low power consumption, enhanced signal coverage, and support for large-scale connections. This demodulation method further enhances the performance of backscatter communication systems, contributing to the realization of the "Internet of Everything" and injecting new impetus into the development of 5G-A and 6G networks.

[0134] 5. This invention improves signal coverage and reliability: Backscatter devices adjust their impedance to reflect signals. Combined with the base station's signal processing algorithm, this technology enhances signal coverage and transmission reliability. This is crucial for the application of IoT devices and sensor networks in complex environments.

[0135] 6. This invention reduces energy consumption: Backscattering technology uses impedance-adjusted reflections of the excitation source signal to communicate, resulting in lower energy consumption than traditional communication methods. This is crucial for the long-term operation and energy management of IoT devices.

[0136] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0137] The above descriptions are only partial embodiments of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A cellular passive Internet of Things backscatter adjacent frequency interference suppression and demodulation method, characterized in that: The method comprises: Obtain an OFDM signal, remove a cyclic prefix, perform fast Fourier transform, and perform decision detection on the OFDM signal to obtain frequency domain symbols, perform IFFT on the frequency domain symbols, and add a cyclic prefix to obtain a modulated signal; The communication channel is estimated to obtain channel parameters, the OFDM signal is deconvolved according to the channel parameters to obtain a transmission signal, and a neural network is trained using the modulated signal and the transmission signal to obtain a reconstruction model; the reconstruction model includes an input layer, five fully connected layers, an activation function, and an output layer, the fully connected layer includes 30 neurons, each of the fully connected layers is connected to one of the activation functions, and the output layer includes two neurons; the input layer includes an envelope term of the input signal, higher-order terms of the envelope term, and various order delay terms; receiving a time-domain OFDM signal, reconstructing the time-domain OFDM signal using the reconstruction model, and obtaining an adjacent-band interference signal generated by leakage of the time-domain OFDM signal using a bandpass filter; Performing Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, thereby obtaining a plurality of interference-eliminated signals; Performing maximum ratio combining on the multiple interference-eliminated signals and then performing maximum likelihood detection to obtain original information corresponding to the time-domain OFDM signal; If the backscattered signal is deployed on the resource blocks at the edges of both sides of the spectrum, the reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signal on one side; If the backscattered signal is deployed on the middle resource block of the spectrum, the reconstruction model is used to reconstruct and eliminate the leakage interference of the cellular signals on both sides.

2. The cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation method according to claim 1, characterized in that: The method of using the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model includes: Using the modulated signal as input and the transmitted signal as output to train a neural network, the neural network continuously adjusting internal parameters during the training process; A loss value is obtained according to the difference between the output of the neural network and the OFDM signal. When the loss value reaches a set threshold, the training ends and a reconstruction model is obtained.

3. The cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation method according to claim 1, characterized in that: The performing maximum ratio combining on the multiple interference-eliminated signals and then performing maximum likelihood detection to obtain original information corresponding to the time-domain OFDM signal includes: Performing maximum ratio combining on the multiple interference-eliminated signals to obtain multiple enhanced signals, and performing maximum likelihood detection on the multiple enhanced signals to obtain original information corresponding to the time-domain OFDM signal; The calculation formula of the maximum likelihood detection is: Wherein, x(l) represents the enhanced signal, y(l) represents the time domain OFDM signal, and L represents the length of the signal.

4. The cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation method according to claim 3, characterized in that: The maximum likelihood detection further includes: According to the maximum likelihood decision criterion The decision maker is calculated: Wherein, N represents the number of receiving antennas, H1 is the assumption that b(l)=1, and H0 is the assumption that b(l)=0.

5. The cellular passive Internet of Things backscattered adjacent frequency interference suppression and demodulation method according to claim 3, characterized in that: The probability distribution of x(l) is 6. A cellular passive Internet of Things backscatter adjacent frequency interference suppression demodulation device, characterized in that: The device comprises: A neural network training module is used to obtain an OFDM signal, perform signal processing and deconvolution on the OFDM signal to obtain a modulated signal and a transmitted signal, and use the modulated signal and the transmitted signal to train a neural network to obtain a reconstruction model; A signal reconstruction module is used to receive a time-domain OFDM signal, reconstruct the time-domain OFDM signal using the reconstruction model, and obtain an adjacent-band interference signal generated by leakage of the time-domain OFDM signal using a bandpass filter; An interference elimination module is used to perform Fourier transform on the time-domain OFDM signal and the adjacent-band interference signal to eliminate interference, thereby obtaining a plurality of interference-eliminated signals; The signal demodulation module is used to perform maximum ratio combining on the multiple interference-eliminated signals and then perform maximum likelihood detection to obtain original information corresponding to the time domain OFDM signal.

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