WO2020238867A1 - Phase noise suppression method and device - Google Patents

Abstract

A phase noise suppression method and device for enhancing phase noise suppression and improving the accuracy of received signals. The method comprises: determining a plurality of states of a symbol; performing phase noise estimation with respect to the plurality of states separately, so as to obtain phase noise values corresponding to the respective states; obtaining a posterior probability for each state; and using the state corresponding to the highest posterior probability as a target received signal. In this way, phase noise suppression can be enhanced, and the accuracy of received signals can be improved.

Description

Method and device for suppressing phase noise

Cross references to related applications

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office, the application number is 201910470679.3, and the application name is "a method and device for phase noise suppression" on May 31, 2019, the entire content of which is incorporated into this application by reference in.

Technical field

This application relates to the field of communication technology, and in particular to a phase noise suppression method and device.

Background technique

In a communication system, after baseband processing and shaping of the signal at the transmitting end, the signal needs to be modulated to the desired frequency range through up-conversion. Correspondingly, the signal needs to be down-converted to baseband at the receiving end. The up-down conversion operation is achieved by mixing with the carrier signal output by the oscillator. Ideally, the carrier signal is an ideal single tone signal with a fixed frequency, so that the baseband signal can be accurately restored after up and down conversion. However, due to non-ideal characteristics such as white noise and flicker noise of the oscillator, the phase of the carrier signal will fluctuate uncertainly and randomly. The random fluctuation of the phase is also equivalent to the instability of the carrier signal frequency, and the output spectrum of the actual oscillator will have noise sidebands.

Phase noise will degrade the quality of the received signal, and misjudgments between signals are prone to cause bit errors. The intensity of phase noise is generally represented by power spectral density (PSD), which describes the power components of random phases at various frequency points. For low-frequency phase noise, there is a strong correlation between signals, and the phase noise is similar within a period of time. It is easier to track the change of phase noise by using this characteristic, thereby suppressing its influence on signal quality. For high-frequency phase noise, which changes rapidly between symbols, it is difficult to predict the phase noise of future signals based on the phase noise of historical signals. In a communication system with a small carrier frequency, such as below 1 GHz, the electronic device technology is relatively mature and the phase noise is small, or the phase noise is mainly concentrated in the low frequency part, which has a small impact on the signal transmission quality. However, for communication systems such as microwaves, the carrier frequency can be as high as several tens of GHz, and the phase noise is relatively significant, so phase noise suppression must be considered in the signal receiving process.

The existing phase noise suppression technology mainly predicts the phase noise value of the future signal based on the historical signal, performs phase compensation based on the predicted phase noise value in the signal receiving process, and then performs decision demodulation and updates the predicted phase noise value. However, this method uses only the phase noise value predicted by the historical signal to demodulate the current signal. Once the predicted phase noise value has a large error, the signal demodulated by the receiving end will have a large error. That is, the phase noise suppression method in the prior art has poor suppression capability, which makes the received signal accuracy low.

Summary of the invention

The present application provides a phase noise suppression method and device, which are used to improve the suppression capability of phase noise and improve the accuracy of received signals.

In the first aspect, this application provides a phase noise suppression method. The method may include: determining the M states of the first symbol according to the first symbol and the modulation mode, and determining the Q of the first symbol according to the M states of the first symbol. States; respectively perform phase noise estimation on each of the Q states to obtain the phase noise value corresponding to each of the Q states; and according to each of the Q states and the phase corresponding to each state Noise value, determine the posterior probability of each state; finally determine that the state corresponding to the largest posterior probability in the posterior probability is the target received signal; where M is an integer greater than 1, and Q is less than or equal to M and greater than 1. Positive integer. In this way, the ability to suppress phase noise can be improved, and the accuracy of the received signal can be improved.

In a possible design, the Q states of the first symbol are determined according to the M states of the first symbol. The specific method can be: determine the decision error of each of the M states; choose the smallest decision error among the M states Q states.

Through the above method, the Q states with the best effect can be selected from the M states, so that the suppression capability of phase noise can be improved, and the accuracy of the received signal can be improved.

In a possible design, before determining the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, determine the phase corresponding to the state with the smallest error among the Q states Perform phase noise prediction on the noise value to obtain a first predicted phase noise value; and perform phase noise compensation on the received symbols located after the first symbol according to the first predicted phase noise value.

Through the above method, the ability to suppress phase noise of symbols after the first symbol can be improved.

In a possible design, after determining that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, perform phase noise prediction according to the phase noise value corresponding to the target received signal to obtain the second predicted phase noise value; The second predicted phase noise value performs phase noise compensation on the received symbols located after the first symbol.

Through the above method, the ability to suppress phase noise of symbols after the first symbol can be improved.

In a possible design, the M states of the first symbol are determined according to the first symbol and the modulation mode. The specific method may be: perform hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol, and determine The M states of the first symbol; the L-level amplitude set is related to the modulation mode, and L is an integer greater than or equal to 1. In this way, M states can be accurately obtained, so that the best state can be subsequently determined as the target received signal, which can improve the accuracy of the received signal.

In a possible design, perform hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol to determine the M states of the first symbol. The specific method can be as follows: Perform the following operations: when L=1, determine M ₁ states according to the first-level amplitude set, and treat M ₁ states as M states; when L is greater than or equal to 2, according to the L-th amplitude set And the Q _L-1 states selected in the L-1 level, determine the Q _L-1 M _L states, and use the Q _L-1 M _L states as the M states; M _L is the amplitude type of the L level Number, M _L is a positive integer; among them, when L=2, the Q ₁ state selected in the first level is based on the amplitude set of the first level and the input value of the first level to get each of the M ₁ states After the corresponding decision error, Q ₁ state is selected from M ₁ states according to M ₁ decision error; when L is greater than or equal to 3, the Q _L-1 state selected at the L-1 level is based on the L After the decision error corresponding to each state in the Q _L-2 M _L-1 state obtained by the amplitude set of -1 and the input value of the L-1 level, the decision error is based on the Q _L-2 M _L-1 Q _L-1 states selected from _L-2 M _L-1 states Q; wherein Q _L is Q.

Through the above method, M states are accurately obtained, so that the best state is subsequently determined as the target received signal, which can improve the accuracy of the received signal.

In a possible design, according to each of the Q states and the phase noise value corresponding to each state, the posterior probability of each state is determined. The specific method can be as follows: determine each state corresponding to each state The conditional probability of the phase noise value; the posterior probability of each state is obtained according to the conditional probability. In this way, the posterior probability of each state can be accurately determined, so that the state with the largest posterior probability can be accurately determined subsequently.

In a possible design, according to each of the Q states and the phase noise value corresponding to each state, the posterior probability of each state is determined. The specific method can be as follows: determine each state corresponding to each state The conditional probability of the phase noise value and the prior probability of the phase noise value corresponding to each state; according to the conditional probability and the prior probability, the posterior probability of each state is obtained. In this way, the posterior probability of each state can be accurately determined, so that the state with the largest posterior probability can be accurately determined subsequently.

In a possible design, before determining the M states of the first symbol according to the first symbol and the modulation mode, the first symbol is phase noise compensated according to the third predicted phase noise value, and the third predicted phase noise value is determined by the receiver Is obtained by predicting the symbol before the first symbol. This can improve the phase noise suppression capability of the first symbol.

In the second aspect, the present application also provides a communication device for phase noise suppression, the communication device having the function of implementing the method example of the first aspect. The function can be realized by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-mentioned functions.

In a possible design, the structure of the communication device includes a storage module and a processing module. These modules can perform the corresponding functions in the method examples of the first aspect. For details, please refer to the detailed description in the method examples of the first aspect. Repeat.

In a possible design, the structure of the communication device includes a memory and a processor, and optionally may also include a communication interface. The communication interface is used to send and receive data and communicate with other devices in the communication system. The processor is configured To support the communication device to perform the corresponding functions in the above method. The memory is coupled with the processor, and it stores program instructions and data necessary for the communication device.

In a third aspect, the present application also provides a computer storage medium in which computer-executable instructions are stored, and the computer-executable instructions are used to make the computer execute any of the above methods when called by the computer.

In a fourth aspect, this application also provides a computer program product containing instructions, which when run on a computer, causes the computer to execute any of the above methods.

In a fifth aspect, the present application also provides a chip, which is coupled with a memory, and is used to read and execute program instructions stored in the memory to implement any of the above methods.

Description of the drawings

FIG. 1 is a schematic diagram of the architecture of a communication system provided by this application;

FIG. 2 is a flowchart of a phase noise suppression method provided by this application;

FIG. 3 is a schematic diagram of a phase noise suppression method provided by this application;

FIG. 4 is a schematic diagram of another phase noise suppression method provided by this application;

FIG. 5 is a schematic diagram of another phase noise suppression method provided by this application;

FIG. 6 is a schematic diagram of obtaining Q states provided by this application;

FIG. 7 is a schematic diagram of a phase noise suppression performance result provided by this application;

FIG. 8 is a schematic diagram of the structure of a communication device provided by this application;

FIG. 9 is a structural diagram of a communication device provided by this application.

Detailed ways

The application will be further described in detail below in conjunction with the accompanying drawings.

The embodiments of the present application provide a phase noise suppression method and device, which are used to improve the suppression capability of phase noise and improve the accuracy of received signals. Among them, the method and device of the present application are based on the same inventive concept. Since the method and the device have similar principles for solving the problem, the implementation of the device and the method can be referred to each other, and the repetition will not be repeated.

In the description of this application, words such as “first” and “second” are only used for the purpose of distinguishing description, and cannot be understood as indicating or implying relative importance, nor as indicating or implying order.

In order to describe the technical solutions of the embodiments of the present application more clearly, the phase noise suppression method and device provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Figure 1 shows the architecture of a possible communication system to which the phase noise suppression method provided by the embodiment of the present application is applicable. The architecture of the communication system includes a first device and a second device, where:

The first device and the second device can communicate with each other. In the communication process, the first device can act as a transmitter to send signals to the second device, and the second device can act as a receiver to receive signals; or the second device can act as a transmitter to send signals to the second device. The first device sends a signal, and the first device serves as a receiving end to receive the signal.

During the communication between the first device and the second device, due to the presence of phase noise in the communication process, the receiving end will perform phase noise suppression, and then demodulate the received signal.

In a possible implementation, the first device and the second device may be two devices that can communicate with each other in a network device, a terminal device, and so on. Among them, the network device can be a wireless access device, and the wireless access device can be a common base station (such as a Node B (NB) or an evolved Node B (eNB)), which can be a wireless network control Radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), home base station (for example, home evolved NodeB, or home Node B, HNB), baseband unit (base band unit, BBU), or wireless fidelity (wireless fidelity, Wifi) access point (AP), which can be a new radio controller (NR controller), or gNode in a 5G system B (gNB), it can be a centralized network element (Centralized Unit), it can be a new wireless base station, it can be a remote radio module, it can be a micro base station, it can be a relay, it can be a distributed network element (Distributed Unit), which may be a reception point (transmission reception point, TRP) or transmission point (transmission point, TP) or any other wireless access device, but the embodiment of the present application is not limited to this, and will not be listed here. Terminal equipment, which can also be called user equipment (UE), mobile station (MS), mobile terminal (MT), etc., is a type of device that provides users with voice and/or data connectivity equipment. For example, terminal devices may include handheld devices with wireless connection capabilities, vehicle-mounted devices, computing devices, mobile stations (MS) or other processing devices connected to wireless modems, etc., as well as one or more A mobile terminal that communicates with the core network. At present, terminal devices can be: mobile phones (mobile phones), tablets, notebook computers, handheld computers, mobile internet devices (MID), wearable devices, virtual reality (VR) devices, augmented reality ( augmented reality (AR) equipment, wireless terminals in industrial control, wireless terminals in self-driving (self-driving), wireless terminals in remote medical surgery, and smart grids (smart grid) The wireless terminal in the transportation safety (transportation safety), the wireless terminal in the smart city (smart city), or the wireless terminal in the smart home (smart home), etc.

It should be noted that the architecture of the communication system shown in FIG. 1 is not limited to only include the devices shown in the figure, and may also include other devices not shown in the figure, which are not specifically listed here in this application.

It should be noted that the communication system shown in FIG. 1 does not constitute a limitation of the communication system applicable to the embodiments of the present application. The communication system can be a variety of communication systems, for example, it can be a long-term evolution (LTE), a fifth-generation (5G) communication system, or a universal terrestrial radio access (UTRA). ), evolved UTRA (E-UTRAN), new radio technology (new radio, NR), GSM/EDGE radio access network-circuit switched domain (GSM EDGE radio access network-circuit switched, GERAN-CS), GSM/EDGE Radio access network-data exchange domain (GSM EDGE radio access network-packet switched, GERAN-PS), code division multiple access (code division multiple access, CDMA) 2000-1XRTT, and multi-radio access technology dual connection (Multi- RAT Dual-Connectivity (MR-DC), etc., can also be a hybrid architecture of multiple communication systems, such as a hybrid architecture of LTE and 5G. Of course, the method in the embodiments of the present application is also applicable to various future communication systems, such as 6G or other communication networks.

The method for suppressing phase noise provided by an embodiment of the present application is applicable to the communication system as shown in FIG. 1. As shown in Figure 2, the specific process of the method may include:

Step 201: Determine M states of the first symbol according to the first symbol and the modulation mode, where M is an integer greater than 1.

Step 202: Determine Q states of the first symbol according to the M states of the first symbol, where Q is a positive integer less than or equal to M and greater than 1.

Step 203: Perform phase noise estimation on each of the Q states respectively, and obtain a phase noise value corresponding to each of the Q states.

Step 204: Determine the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state.

Step 205: Determine the state corresponding to the largest posterior probability among the posterior probabilities as the target received signal.

Using the phase noise suppression method provided by the embodiments of the present application, multiple states of a symbol can be determined, and phase noise estimation of the multiple states can be performed to obtain the phase noise value corresponding to each state, and then the posterior probability of each state can be obtained , Regard the state corresponding to the largest posterior probability as the target received signal. This can improve the suppression of phase noise and improve the accuracy of the received signal.

It should be noted that the execution subject of the above steps can be the first device or the second device in the communication system shown in FIG. 1, or it can be any one of the examples of the first device and the second device listed above. Equipment, this application does not limit this.

In an optional implementation manner, the M states of the first symbol are determined according to the first symbol and the modulation mode. The specific method may be: performing a hierarchical decision solution on the first symbol according to the L-level amplitude set of the first symbol Adjust to determine the M states of the first symbol; the amplitude set of level L is related to the modulation mode, and L is an integer greater than or equal to 1.

Furthermore, the Q states of the first symbol are determined according to the M states of the first symbol. The specific method may be: determining the decision error of each state in the M states; selecting the Q states with the smallest decision error from the M states.

In this application, the above process of obtaining M states and then obtaining Q states may be collectively referred to as a process of decision demodulation. Illustratively, FIG. 3 shows a schematic diagram of a phase noise suppression method of the present application. In Figure 3, the first symbol is x. The ideal transmission signal s is affected by phase noise θ, white noise n, etc., and the symbol that reaches the receiving end is x. The goal of phase noise suppression is to recover s from x with as little error probability as possible. It can be seen from Figure 3 that the decision and demodulation of x to obtain Q states can be respectively

Then the phase noise is estimated for each state separately, and the phase noise value corresponding to each state is obtained as

Then the posterior probabilities corresponding to each state are p ₁ , p ₂ , ..., p _Q , and finally based on the maximum a posteriori (MAP) criterion, the state with the largest posteriori probability is recorded as

will

Receive the signal as a target.

In an exemplary implementation manner, phase noise prediction is performed, and the predicted phase noise value is subjected to phase noise compensation for symbols after the first symbol. Specifically, it can be divided into the following two examples:

The first example: before determining the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, determine the phase noise value corresponding to the state with the smallest error among the Q states Perform phase noise prediction to obtain a first predicted phase noise value; according to the first predicted phase noise value, perform phase noise compensation on the received symbols located after the first symbol.

Exemplarily, when the first example is adopted, a schematic diagram of a specific phase noise suppression method may be as shown in FIG. 4. In Figure 4, it can be understood as the state

The decision error of is the smallest, according to

Corresponding phase noise value

Perform phase noise prediction to obtain the first predicted phase noise value as

Then according to

Perform phase noise compensation on the received symbol located after the first symbol.

Using the first example, the loop feedback delay can be reduced, and it can adapt to situations that are more sensitive to the loop delay.

The second example: After determining that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, perform phase noise prediction according to the phase noise value corresponding to the target received signal to obtain the second predicted phase noise value; The predicted phase noise value performs phase noise compensation on the received symbols located after the first symbol.

Exemplarily, when the second example is adopted, a schematic diagram of a specific phase noise suppression method may be as shown in FIG. 5. In Figure 5, the selected state

Corresponding phase noise value

according to

Perform phase noise prediction to obtain the second predicted phase noise value as

Then according to

Perform phase noise compensation on the received symbol located after the first symbol.

Using the second example, the reliability of phase noise suppression can be higher.

It should be noted that the symbol located after the first symbol may be the next symbol of the first symbol, may also be any symbol after the next symbol of the first symbol, or may be several matches after the first symbol. This application does not limit this.

In an optional implementation manner, before performing step 201, before determining the M states of the first symbol according to the first symbol and the modulation mode, perform phase noise compensation on the first symbol according to the third predicted phase noise value , The third predicted phase noise value is obtained by predicting the received symbol before the first symbol. Wherein, the symbol located before the first symbol may be the previous symbol of the first symbol, any symbol before the previous symbol of the first symbol, or several matches before the first symbol. This is not limited.

In a specific implementation manner, the first symbol is subjected to hierarchical decision demodulation according to the L-level amplitude set of the first symbol to determine the M states of the first symbol. The specific method may be:

Perform the following operations for the L level of the first symbol:

When L=1, M ₁ states are determined according to the first-level amplitude set, and M ₁ states are regarded as M states;

When L is greater than or equal to 2, according to the amplitude set of the L-th level and the Q _L-1 states selected in the L-1 level, determine the Q _L-1 M _L states, and set Q _L-1 M _L The states are regarded as M states; M _L is the number of amplitude types of the L-th level, and M _L is a positive integer;

Among them, when L=2, the Q ₁ state selected at the first level is based on the amplitude set of the first level and the input value of the first level to obtain the decision error corresponding to each state in the M ₁ state, according to M _a decision errors selected from M ₁ states in _a state Q; L is greater than or equal to 3, the Q states the first _L-1 L-1 level selection is based on the magnitude of the first set and the level L-1 After the decision error corresponding to each state in the Q _L-2 M _L-1 state is obtained from the input value of the L-1 level, according to the Q _L-2 M _L-1 decision error, the Q _L-2 M _L- Q _L-1 states in _a selected state; wherein Q _L is Q.

For example, suppose the amplitude set of the Lth level is

The input values of the L-th stage are y ₁ , y ₂ ,..., y _L , then the M states of the first symbol are obtained and then the Q states of the first symbol are obtained. The specific implementation process is as follows:

For the first level, according to the amplitude set of the first level, the M ₁ states are:

Then, according to the amplitude set of the first level and the input value y _{1 of the} first level, the decision errors corresponding to each state in the M ₁ states are:

Then you can choose the Q ₁ state with the smallest decision error, denoted as

The corresponding decision errors of Q ₁ states are

If there is only one level, then M ₁ states can be regarded as M states, and Q ₁ states can be regarded as Q states. If there are multiple levels, continue the following process.

For the second level, according to the amplitude set of the second level and the Q ₁ states selected by the first level, the Q ₁ M ₂ states are obtained respectively:

Then according to the amplitude set of the second stage, the input value of the second stage and the Q ₁ decision error of the first stage, the decision errors corresponding to each of the Q ₁ M ₂ states are obtained as:

Then select the Q ₂ states with the smallest decision error among the Q ₁ M ₂ states, denoted as

The corresponding decision errors of Q ₂ states are

For the L level, according to the amplitude set of the L level and the Q _L states selected by the L-1 level, the Q _L-1 M _L states are obtained as follows:

Then according to the amplitude set of the L-th stage, the input value of the L-th stage and the Q _L-1 decision error of the L-1 stage, the decision error corresponding to each state in the Q _L-1 M _L states is obtained as:

Then the Q _L states with the smallest decision error can be selected, denoted as

And Q _L states are regarded as Q states. Among them, Q _{L is} less than or equal to Q _L-1 M _L.

Specifically, the foregoing implementation process can be implemented as shown in FIG. 6.

In an optional implementation manner, according to each of the Q states and the phase noise value corresponding to each state, the posterior probability of each state is determined. The specific method may be: determining each state and each state The conditional probability of the phase noise value corresponding to the state; the posterior probability of each state is obtained according to the conditional probability.

Exemplarily, when the symbol prior is equal, the posterior probability p _{k of} each state may conform to the following formula:

among them,

Status

Corresponding phase noise value

The conditional probability of, which depends on the mean square error (MSE), is mainly determined by the residual phase noise and additive white noise after phase noise estimation, and is generally Gaussian.

In another optional implementation manner, the posterior probability of each state is determined according to each of the Q states and the phase noise value corresponding to each state. The specific method may be: determining each state and each state The conditional probability of the phase noise value corresponding to each state, and the prior probability of the phase noise value corresponding to each state; according to the conditional probability and the prior probability, the posterior probability of each state is obtained.

Exemplarily, when the symbol prior is equal, the posterior probability p _{k of} each state may conform to the following formula:

k=1,2,...,Q, where,

Status

Corresponding phase noise value

The prior probability of which can also be called the regularization factor.

After obtaining the delay probability of each state based on the above method, the state with the largest posterior probability can be selected as the target received signal through the MAP process shown in Figs. 3 to 5, for example.

It should be noted that in step 203, the involved phase noise estimation method may apply the existing phase noise estimation method, such as the Kalman filter algorithm, which is not described in detail here in this application.

Based on the above embodiments, the performance results of phase noise suppression after adopting the phase noise suppression method proposed in this application and the method in the prior art under the same scenario. For example, the scenario is that the modulation method is 4096QAM, the signal-to-noise ratio (SNR) is 47dB, and the symbol rate is 50M; the number of channels is a single-input single-output (SISO) single channel; The phase noise prediction and estimation algorithm adopts the Kalman filter algorithm; the phase noise PSD model adopts the following formula, where the pole frequency fp=100KHz, the zero frequency fz=100MHz, when f=100KHz, the PSD is -90dBc/Hz:

FIG. 7 shows a schematic diagram of PSD comparison before and after phase noise compensation using the method of the present application in the above-mentioned scenario, and a PSD curve after phase noise compensation in the prior art solution. It can be seen that, compared with the existing solution, the PSD of the residual phase noise of the technical solution of the present application is significantly lower, and each frequency point can improve the suppression ability by about 5dB. It should be noted that the high frequency part of the phase noise in the prior art scheme in Figure 7 is stronger than before compensation. This is because the Kalman filter algorithm pursues the best overall suppression capability, but it cannot guarantee that after each frequency point is compensated The residual phase noise is smaller. In comparison, the technical solution of the present application can also have a certain suppression effect on the high frequency part of the phase noise.

Table 1 shows the MSE and bit error rate (BER) results corresponding to FIG. 7. It can be seen that, compared with the existing technical solution, due to the stronger phase noise suppression capability, the present application can increase the MSE by more than 5 dB, and the BER can also be significantly reduced.

Table 1

method	MSE	BER
No compensation	34.7dB	2.2e-2
Prior art method	38.1dB	5.8e-3
How to apply	43.5dB	1.5e-3

It can be seen from the foregoing that the phase noise suppression method provided by the embodiment of the present application can improve the suppression capability of phase noise and can improve the accuracy of the received signal.

In the above-mentioned embodiments provided in the present application, it can be understood that, in order to realize the above-mentioned functions, the communication device includes hardware structures and/or software modules corresponding to each function. Those skilled in the art should easily realize that, in combination with the modules and algorithm steps of the examples described in the embodiments disclosed in this application, this application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed by hardware or computer software-driven hardware depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.

For example, when the corresponding function is implemented by a software module, the communication device for phase noise suppression may include a storage module 801 and a processing module 802. For details, refer to the schematic structural diagram shown in FIG. 8.

In an embodiment, the communication device shown in FIG. 8 may be used to implement the phase noise suppression method of the embodiment shown in FIG. 2 above. E.g:

The storage module 801 is used to store computer programs; the processing module 802 is used to call the computer programs stored in the storage module to execute:

Determine the M states of the first symbol according to the first symbol and the modulation mode, where M is an integer greater than 1;

Determine the Q states of the first symbol according to the M states of the first symbol, where Q is a positive integer less than or equal to M and greater than 1;

Perform phase noise estimation on each of the Q states respectively, and obtain the phase noise value corresponding to each of the Q states;

Determine the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state;

Determine the state corresponding to the largest posterior probability in the posterior probability as the target received signal.

Therefore, based on the above communication device, multiple states of a symbol can be determined, and phase noise estimation of multiple states can be performed to obtain the phase noise value corresponding to each state, and then the posterior probability of each state can be obtained. The state corresponding to the probability is used as the target received signal. In this way, the ability to suppress phase noise can be improved, and the accuracy of the received signal can be improved.

Specifically, when the processing module 802 determines the Q states of the first symbol according to the M states of the first symbol, it is specifically configured to: determine the decision error of each of the M states; select the smallest decision error among the M states Q states.

In an example, before determining the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, the processing module 802 is also used to determine the smallest error according to the Q states Perform phase noise prediction on the phase noise value corresponding to the state to obtain the first predicted phase noise value; perform phase noise compensation on the received symbols located after the first symbol according to the first predicted phase noise value.

In another example, after determining that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, the processing module 802 is further used to: perform phase noise prediction according to the phase noise value corresponding to the target received signal to obtain the second prediction Phase noise value; according to the second predicted phase noise value, phase noise compensation is performed on the received symbols located after the first symbol.

In an optional implementation manner, when the processing module 802 determines the M states of the first symbol according to the first symbol and the modulation mode, it is specifically configured to: perform the first symbol according to the L-level amplitude set of the first symbol Hierarchical decision demodulation determines the M states of the first symbol; the amplitude set of level L is related to the modulation mode, and L is an integer greater than or equal to 1.

Specifically, when the processing module 802 performs hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol to determine the M states of the first symbol, it is specifically configured to: Do the following:

When L=1, M ₁ states are determined according to the first-level amplitude set, and M ₁ states are regarded as M states;

When L is greater than or equal to 2, according to the amplitude set of the L-th level and the Q _L-1 states selected in the L-1 level, determine the Q _L-1 M _L states, and set Q _L-1 M _L The states are regarded as M states; M _L is the number of amplitude types of the L-th level, and M _L is a positive integer;

Among them, when L=2, the Q ₁ state selected at the first level is based on the amplitude set of the first level and the input value of the first level to obtain the decision error corresponding to each state in the M ₁ state, according to M _a decision errors selected from M ₁ states in _a state Q; L is greater than or equal to 3, the Q states the first _L-1 L-1 level selection is based on the magnitude of the first set and the level L-1 After the decision error corresponding to each state in the Q _L-2 M _L-1 state is obtained from the input value of the L-1 level, according to the Q _L-2 M _L-1 decision error, the Q _L-2 M _L- Q _L-1 states in _a selected state; wherein Q _L is Q.

In one implementation, when the processing module 802 determines the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, it is specifically used to: determine each state and each state The conditional probability of the phase noise value corresponding to the state; the posterior probability of each state is obtained according to the conditional probability.

In another implementation manner, when the processing module 802 determines the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, it is specifically used to: determine each state and each state The conditional probability of the phase noise value corresponding to each state, and the prior probability of the phase noise value corresponding to each state; according to the conditional probability and the prior probability, the posterior probability of each state is obtained.

In a possible implementation manner, before determining the M states of the first symbol according to the first symbol and the modulation mode, the processing module 802 is further configured to: perform phase noise compensation on the first symbol according to the third predicted phase noise value, The third predicted phase noise value is obtained by predicting the received symbol before the first symbol.

In addition, based on the processing module 802 in the communication device for phase noise suppression, other operations or functions in the above method can also be implemented, which will not be repeated here.

It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation. The functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disk or optical disk and other media that can store program code .

For another example, when the corresponding function is implemented by hardware, the communication device for phase noise suppression can be the processor 902, optionally can also include the memory 903, and optionally can also include the communication interface 901. For details, refer to Fig. 9 shows the structure diagram.

The processor 902 may be a central processing unit (CPU), a network processor (NP), a combination of a CPU and an NP, or the like. The processor 902 may further include a hardware chip. The aforementioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof. When the processor 902 implements the above functions, it may be implemented by hardware, and of course, it may also be implemented by hardware executing corresponding software.

The communication interface 901 and the processor 902 are connected to each other. Optionally, the communication interface 901 and the processor 902 are connected to each other through a bus 904; the bus 904 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. . The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in FIG. 9, but it does not mean that there is only one bus or one type of bus.

The memory 903 is coupled with the processor 902 and used for storing programs and the like. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory 903 may include RAM, or may also include non-volatile memory, such as at least one disk memory. The processor 902 executes the application program stored in the memory 903 to implement the above-mentioned functions, thereby implementing the phase noise suppression method shown in FIG. 2.

In an embodiment, the communication device shown in FIG. 9 may be used to implement the phase noise suppression method in the embodiment shown in FIG. 2 above. E.g:

The memory 903 is used to store computer programs; the processor 902 is used to call the computer programs stored in the storage module to execute:

Determine the M states of the first symbol according to the first symbol and the modulation mode, where M is an integer greater than 1;

Determine the Q states of the first symbol according to the M states of the first symbol, where Q is a positive integer less than or equal to M and greater than 1;

Perform phase noise estimation on each of the Q states respectively, and obtain the phase noise value corresponding to each of the Q states;

Determine the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state;

Determine the state corresponding to the largest posterior probability in the posterior probability as the target received signal.

Therefore, based on the above communication device, multiple states of a symbol can be determined, and phase noise estimation of multiple states can be performed to obtain the phase noise value corresponding to each state, and then the posterior probability of each state can be obtained. The state corresponding to the probability is used as the target received signal. In this way, the ability to suppress phase noise can be improved, and the accuracy of the received signal can be improved.

Specifically, when the processor 902 determines the Q states of the first symbol according to the M states of the first symbol, it is specifically configured to: determine the decision error of each of the M states; select the smallest decision error among the M states Q states.

In an example, before determining the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, the processor 902 is further configured to: determine the smallest error according to the Q states Perform phase noise prediction on the phase noise value corresponding to the state to obtain the first predicted phase noise value; perform phase noise compensation on the received symbols located after the first symbol according to the first predicted phase noise value.

In another example, after the processor 902 determines that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, it is further configured to: perform phase noise prediction according to the phase noise value corresponding to the target received signal to obtain the second prediction Phase noise value; according to the second predicted phase noise value, phase noise compensation is performed on the received symbols located after the first symbol.

In an optional implementation manner, when the processor 902 determines the M states of the first symbol according to the first symbol and the modulation mode, it is specifically configured to: perform the first symbol on the first symbol according to the L-level amplitude set of the first symbol Hierarchical decision demodulation determines the M states of the first symbol; the amplitude set of level L is related to the modulation mode, and L is an integer greater than or equal to 1.

Specifically, when the processor 902 performs hierarchical decision demodulation on the first symbol according to the L level amplitude set of the first symbol to determine the M states of the first symbol, it is specifically configured to: Do the following:

When L=1, M ₁ states are determined according to the first-level amplitude set, and M ₁ states are regarded as M states;

When L is greater than or equal to 2, according to the amplitude set of the L-th level and the Q _L-1 states selected in the L-1 level, determine the Q _L-1 M _L states, and set Q _L-1 M _L The states are regarded as M states; M _L is the number of amplitude types of the L-th level, and M _L is a positive integer;

Among them, when L=2, the Q ₁ state selected at the first level is based on the amplitude set of the first level and the input value of the first level to obtain the decision error corresponding to each state in the M ₁ state, according to M _a decision errors selected from M ₁ states in _a state Q; L is greater than or equal to 3, the Q states the first _L-1 L-1 level selection is based on the magnitude of the first set and the level L-1 After the decision error corresponding to each state in the Q _L-2 M _L-1 state is obtained from the input value of the L-1 level, according to the Q _L-2 M _L-1 decision error, the Q _L-2 M _L- Q _L-1 states in _a selected state; wherein Q _L is Q.

In one implementation, when the processor 902 determines the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, it is specifically used to: determine each state and each state The conditional probability of the phase noise value corresponding to the state; the posterior probability of each state is obtained according to the conditional probability.

In another implementation manner, when the processor 902 determines the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state, it is specifically used to: determine each state and each state The conditional probability of the phase noise value corresponding to each state, and the prior probability of the phase noise value corresponding to each state; according to the conditional probability and the prior probability, the posterior probability of each state is obtained.

In a possible implementation manner, before determining the M states of the first symbol according to the first symbol and the modulation mode, the processor 902 is further configured to: perform phase noise compensation on the first symbol according to the third predicted phase noise value, The third predicted phase noise value is obtained by predicting the received symbol before the first symbol.

In one embodiment, the communication interface 901 is used to implement communication interaction between other devices or devices connected to the communication device, that is, to receive and send data or signals; for example, the communication interface 901 is used to receive the first symbol.

In this application, "plurality" means two or more.

Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

This application is described with reference to flowcharts and/or block diagrams of methods, equipment (systems), and computer program products according to the embodiments of this application. It should be understood that each process and/or block in the flowchart and/or block diagram, and the combination of processes and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing equipment to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing equipment are generated It is a device that realizes the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device. The device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment. The instructions provide steps for implementing functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in the block diagram.

Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the scope of the embodiments of the present application. In this way, if these modifications and variations of the embodiments of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims (18)

1. A phase noise suppression method, characterized in that it comprises:

   Determine the M states of the first symbol according to the first symbol and the modulation mode, where M is an integer greater than 1;

   Determine Q states of the first symbol according to the M states of the first symbol, where Q is a positive integer less than or equal to M and greater than 1;

   Respectively performing phase noise estimation on each of the Q states, and obtaining a phase noise value corresponding to each of the Q states;

   Determine the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state;

   It is determined that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal.
2. The method of claim 1, wherein determining the Q states of the first symbol according to the M states of the first symbol comprises:

   Determine the decision error of each of the M states;

   Select Q states with the smallest decision error among the M states.
3. The method according to claim 1 or 2, characterized in that before determining the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state , The method further includes:

   Performing phase noise prediction according to the phase noise value corresponding to the state with the smallest decision error among the Q states to obtain the first predicted phase noise value;

   According to the first predicted phase noise value, phase noise compensation is performed on the received symbols located after the first symbol.
4. The method according to claim 1 or 2, wherein after determining that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, the method further comprises:

   Performing phase noise prediction according to the phase noise value corresponding to the target received signal to obtain a second predicted phase noise value;

   Perform phase noise compensation on the received symbols located after the first symbol according to the second predicted phase noise value.
5. The method according to any one of claims 1 to 4, wherein determining the M states of the first symbol according to the first symbol and the modulation mode comprises:

   Perform hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol to determine the M states of the first symbol; the L-level amplitude set is related to the modulation mode, L Is an integer greater than or equal to 1.
6. The method according to claim 5, wherein the stepwise decision demodulation of the first symbol according to the L-level amplitude set of the first symbol to determine the M states of the first symbol comprises:

   Perform the following operations for the Lth level of the first symbol:

   When L=1, determine M ₁ states according to the amplitude set of the first level, and use the M ₁ states as the M states;

   When L is greater than or equal to 2, the Q _L-1 M _L states are determined according to the amplitude set of the L-th level and the Q _L-1 states selected in the L-1 level, and the Q _{L-1 ML} states as the M states; _ML is the number of amplitude types of the L-th level, _ML is a positive integer;

   Wherein, when L=2, the Q ₁ state selected at the first level is based on the amplitude set of the first level and the input value of the first level to obtain the corresponding state of each of the M ₁ states After the error is judged, Q ₁ states are selected from the M ₁ states according to the M ₁ decision errors; when L is greater than or equal to 3, the Q _L-1 states selected at the L-1 level are based on After the decision error corresponding to each state in the Q _L-2 M _L-1 state obtained by the amplitude set of the L-1 level and the input value of the L-1 level, according to Q _{L- 2} M _L-1 number of decision errors selected from _L-2 M _L-1 Q states in the states Q _L-1; wherein Q _L is Q.
7. The method according to any one of claims 1-6, wherein the posterior of each state is determined according to each of the Q states and the phase noise value corresponding to each state Probabilities include:

   Determining the conditional probability of the phase noise value corresponding to each state and each state;

   The posterior probability of each state is obtained according to the conditional probability.
8. The method according to any one of claims 1-6, wherein the posterior of each state is determined according to each of the Q states and the phase noise value corresponding to each state Probabilities include:

   Determining the conditional probability of the phase noise value corresponding to each state and each state, and the prior probability of the phase noise value corresponding to each state;

   According to the conditional probability and the prior probability, the posterior probability of each state is obtained.
9. The method according to any one of claims 1-8, wherein before determining the M states of the first symbol according to the first symbol and the modulation mode, the method further comprises:

   Performing phase noise compensation on the first symbol according to a third predicted phase noise value, where the third predicted phase noise value is obtained by predicting a received symbol before the first symbol.
10. A communication device, characterized by comprising:

    Storage module for storing computer programs;

    The processing module is used to call the computer program stored in the storage module to execute:

    Determine the M states of the first symbol according to the first symbol and the modulation mode, where M is an integer greater than 1;

    Determine Q states of the first symbol according to the M states of the first symbol, where Q is a positive integer less than or equal to M and greater than 1;

    Respectively performing phase noise estimation on each of the Q states, and obtaining a phase noise value corresponding to each of the Q states;

    Determine the posterior probability of each state according to each of the Q states and the phase noise value corresponding to each state;

    It is determined that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal.
11. The communication device according to claim 10, wherein the processing module is specifically configured to: when determining the Q states of the first symbol according to the M states of the first symbol:

    Determine the decision error of each of the M states;

    Select Q states with the smallest decision error among the M states.
12. The communication device according to claim 10 or 11, wherein the processing module determines each of the Q states according to each state and the phase noise value corresponding to each state Before the posterior probability of the state, it is also used to:

    Performing phase noise prediction according to the phase noise value corresponding to the state with the smallest decision error among the Q states to obtain the first predicted phase noise value;

    According to the first predicted phase noise value, phase noise compensation is performed on the received symbols located after the first symbol.
13. The communication device according to claim 10 or 11, wherein the processing module, after determining that the state corresponding to the largest posterior probability among the posterior probabilities is the target received signal, is further configured to:

    Performing phase noise prediction according to the phase noise value corresponding to the target received signal to obtain a second predicted phase noise value;

    Perform phase noise compensation on the received symbols located after the first symbol according to the second predicted phase noise value.
14. The communication device according to any one of claims 10-13, wherein the processing module is specifically configured to: when determining the M states of the first symbol according to the first symbol and the modulation mode:

    Perform hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol to determine the M states of the first symbol; the L-level amplitude set is related to the modulation mode, L Is an integer greater than or equal to 1.
15. The communication device according to claim 14, wherein the processing module performs hierarchical decision demodulation on the first symbol according to the L-level amplitude set of the first symbol to determine the first symbol When the M states of, are specifically used for:

    Perform the following operations for the Lth level of the first symbol:

    When L=1, determine M ₁ states according to the amplitude set of the first level, and use the M ₁ states as the M states;

    When L is greater than or equal to 2, the Q _L-1 M _L states are determined according to the amplitude set of the L-th level and the Q _L-1 states selected in the L-1 level, and the Q _{L-1 ML} states as the M states; _ML is the number of amplitude types of the L-th level, _ML is a positive integer;

    Wherein, when L=2, the Q ₁ state selected at the first level is based on the amplitude set of the first level and the input value of the first level to obtain the corresponding state of each of the M ₁ states After the error is judged, Q ₁ states are selected from the M ₁ states according to the M ₁ decision errors; when L is greater than or equal to 3, the Q _L-1 states selected at the L-1 level are based on After the decision error corresponding to each state in the Q _L-2 M _L-1 state obtained by the amplitude set of the L-1 level and the input value of the L-1 level, according to Q _{L- 2} M _L-1 number of decision errors selected from _L-2 M _L-1 Q states in the states Q _L-1; wherein Q _L is Q.
16. The communication device according to any one of claims 10-15, wherein the processing module determines the phase noise value corresponding to each of the Q states and When describing the posterior probability of each state, it is specifically used to:

    Determining the conditional probability of the phase noise value corresponding to each state and each state;

    The posterior probability of each state is obtained according to the conditional probability.
17. The communication device according to any one of claims 10-15, wherein the processing module determines the phase noise value corresponding to each of the Q states and When describing the posterior probability of each state, it is specifically used to:

    Determining the conditional probability of the phase noise value corresponding to each state and each state, and the prior probability of the phase noise value corresponding to each state;

    According to the conditional probability and the prior probability, the posterior probability of each state is obtained.
18. The communication device according to any one of claims 10-17, wherein the processing module is further configured to: before determining the M states of the first symbol according to the first symbol and the modulation mode:

    Performing phase noise compensation on the first symbol according to a third predicted phase noise value, where the third predicted phase noise value is obtained by predicting a received symbol before the first symbol.

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