Variable damping factor message passing method based on imitation radio frequency division multiplexing

Abstract

The invention belongs to the field of signal detection in signal processing, and particularly relates to a variable damping factor message passing method based on imitation radio frequency division multiplexing. Aiming at the problem that a damping factor in a traditional message passing method is a fixed value, the invention creatively designs a message passing method for solving an optimal variable damping factor; the method comprises the following steps: generating damping factors at equal intervals L between 0 and 1, performing signal detection on an AFDM signal, detecting each damping factor, and according to a bit error rate obtained after detection, taking average bit error rate performance as a first weight and combining average iterations as a second weight to obtain an optimal damping factor considering both the average bit error rate BER and the average iterations; and then the optimal damping factor is substituted into a message passing method to finally obtain an output signal, and the method simultaneously meets the requirements of low complexity, high bit error rate performance and global optimum realization.

Description

Technical Field

[0001] The invention belongs to the field of signal detection in signal processing, and in particular relates to a variable damping factor message transmission method based on AFDM (Artificial Radio Frequency Division Multiplexing). Background Art

[0002] Usually, wireless signals are contaminated by noise during the propagation process. After receiving the signal at the receiving end, signal detection is usually required to restore it to the original signal. Existing AFDM signal detection methods such as maximum likelihood-based signal detection method, minimum mean square error-based signal detection method, maximum ratio combining-based signal detection method, and message passing-based signal detection method have poor detection effects on AFDM signals.

[0003] The signal detection method based on maximum likelihood and the signal detection method based on minimum mean square error have high complexity and very limited performance; the signal detection method based on maximum ratio combining has reduced complexity, but its performance is similar to that based on minimum mean square error; the signal detection method based on message passing has both low complexity and good performance, but the damping factor is currently a given constant and cannot achieve global optimization. The following Table 1 shows the characteristics of existing signal detection methods for AFDM signal detection:

[0004] Name of existing signal detection method Complexity Features Based on Maximum Likelihood (ML) high Low bit error rate performance Based on Minimum Mean Square Error (MMSE) high Low bit error rate performance Based on Maximum Ratio Combining (MRC) Low Low bit error rate performance Message Passing (MP) based Low High bit error rate performance, unable to achieve global optimality

[0005] In summary, there is currently a lack of a signal detection method for AFDM signals that can achieve low complexity and high bit error rate performance and can achieve global optimization. Summary of the invention

[0006] In view of the above problems or shortcomings, in order to take into account the three requirements of low complexity, high bit error rate performance and global optimization at the same time, the present invention provides a variable damping factor message transmission method based on AFDM.

[0007] A variable damping factor message transmission method based on AFDM includes the following steps:

[0008] Step 1: For the received AFDM demodulated signal, generate damping factors at intervals L between 0 and 1 and number them as Δ(u), u = 1, 2, ..., N L , where N L Represents the number of damping factors generated when the interval is L.

[0009] Step 2: Substitute Δ(u) into the message passing method for iteration and calculate the corresponding average bit error rate BER(u) and average number of iterations

[0010] Furthermore, the calculation formula of the BER(u) is as follows:

[0011]

[0012] in, The symbol represents the XOR operation, N represents the number of AFDM symbols, n iter represents the number of times the Monte Carlo method is repeated, Q represents the order of quadrature amplitude modulation (QAM), that is, the length of the QAM alphabet, and a (t) [c] and Represent the cth element of the message signal and the estimated signal respectively, t is in n iter Take the value from.

[0013] Furthermore, the average number of iterations The calculation formula is as follows:

[0014]

[0015] in Represents the number of iterations of the message passing method.

[0016] Step 3: Find the minimum average bit error rate BER min .

[0017] Furthermore, BER min The calculation formula is as follows:

[0018]

[0019] Step 4: Find the damping factor Δ(u) that can achieve the best BER and average number of iterations.

[0020] Initialize the optimal damping factor Δ(u) with the number TminT=1, and the average number of iterations T min for Traverse Δ(u); when |BER min -BER(u)|<γ, 0<γ<1, and renew minT=u.

[0021] Since BER performance is more important than the average number of iterations in communication, the average number of iterations should be kept as small as possible while ensuring the optimal BER performance. Therefore, BER(u) and BER are not considered. min Larger difference.

[0022] Step 5: The optimal damping factor selected in step 4 is Δ(minT), which is substituted into the message passing method to obtain the BER.

[0023] Furthermore, the interval L for generating the damping factor is adjusted in size according to the actual application scenario; if the performance requirement is high and the computing resources are abundant, it is reduced; otherwise, it is increased.

[0024] In summary, in order to solve the problem that the damping factor in the traditional message transmission method is a fixed value, the present invention designs a message transmission method for solving the optimal variable damping factor, performs signal detection on the AFDM signal, and performs steps 1-5 in sequence to obtain the optimal damping factor with the best BER and the least average number of iterations; then the optimal damping factor is introduced into the message transmission method to finally obtain the output signal. The present invention simultaneously meets the requirements of high bit error rate performance and achieving global optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of the present invention;

[0026] Figure 2 It is a comparison diagram of BER performance between the comparative example message passing method and the variable damping factor message passing method of the embodiment. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0028] Example

[0029] A variable damping factor message transmission method based on AFDM, the process is as follows: Figure 1 As shown:

[0030] First, set the number of AFDM symbols N = 256, and use 4-QAM modulation, that is, Q = 4. Then, use MATLAB software to randomly generate Nlog2 (Q) bits of data with values ​​of 0 or 1, and use QAM modulation to obtain the AFDM message signal. The AFDM message signal is modulated and then passes through the channel. The channel parameters are set as follows: the number of paths P = 4, the normalized Doppler shift and delay corresponding to each path are set to [0, 1, 2, 3] and [0, 1, 2, 3] respectively, and the channel gain is generated by an independent complex Gaussian distribution random variable with a mean of 0 and a variance of 1 / P. After receiving the signal at the receiving end, it is demodulated to obtain the AFDM demodulated signal.

[0031] Step 1: For the received AFDM demodulated signal, generate damping factors between 0 and 1 at intervals of 0.1 and number them as Δ(u), where u=1, 2, ..., 10.

[0032] Step 2: Substitute Δ(u) into the message passing method for iteration and calculate the average bit error rate BER(u) and the average number of iterations. This embodiment sets the number of repeated calculations n of the Monte Carlo method iter is 10000.

[0033] The calculation formula of BER(u) is as follows:

[0034]

[0035] Average number of iterations The calculation formula is as follows:

[0036]

[0037] Step 3: Find the minimum average bit error rate BER min , the calculation formula is as follows:

[0038]

[0039] Step 4: Find the damping factor Δ(u) that can achieve the optimal average bit error rate BER and average number of iterations;

[0040] Initialize the optimal damping factor Δ(u) with the serial number minT=1, and the average number of iterations T min for Traverse Δ(u); when |BER min -BER(u)|<1×10 -5 ,and When, update minT=u.

[0041] Step 5: The optimal damping factor selected in step 4 is Δ(minT), which is substituted into the message passing method to obtain the BER. The specific existing message passing method is shown in the comparative example.

[0042] Comparative Example

[0043] The AFDM demodulated signal used in the embodiment is directly post-processed using the existing AFDM-based message passing method, which specifically includes the following steps:

[0044] Step 1: Calculate the average value of interference in the channel and variance

[0045]

[0046] Among them, H eff represents the channel matrix, I(d) is the index value corresponding to the non-zero element in the dth row of the channel matrix, and a j is the jth element in the QAM alphabet A, σ 2 Represents the power of the noise.

[0047] Step 2: Update the probability mass function

[0048]

[0049] where Δ∈(0, 1] is a damping factor used to improve the performance by controlling the convergence speed, and:

[0050]

[0051] Where X is the message signal, y is the demodulated signal, and:

[0052]

[0053] Step 3: Calculate the convergence index η (i) .

[0054]

[0055] Where γ is a number greater than 0 and less than 1, and is set to 0.05 in this comparative example. is an indicator function that gives the value 1 if the expression in the argument is true, and 0 otherwise, and:

[0056]

[0057] Wherein, J(c) represents the index of the non-zero element in the c-th column of the channel matrix.

[0058] Step 4: Update decision.

[0059] If η (i) >η (i-1) , then the decision of transmitting symbols is updated as:

[0060]

[0061] Step 5: Stop Condition. The message delivery method stops when at least one of the following conditions is met:

[0062] (1)η (i) =1,

[0063] (2) where i * ∈{1,…,i-1} is such that The maximum iteration index,

[0064] (3) Reach the maximum number of iterations n iter .

[0065] Set ∈ = 0.2 to ignore small fluctuations in η.

[0066] Test analysis: In the computer MATLAB environment, for the received AFDM demodulated signal, the number of symbols, channel matrix, and noise power are passed into the above method, and these parameters are brought into the formula of the variable damping factor message transmission method of the above embodiment for calculation, and finally the BER performance of the existing message transmission method of the comparative example and the variable damping factor message transmission method of the embodiment is obtained, as shown in FIG. Figure 2 As shown. Figure 2 It can be seen that under the condition of a signal-to-noise ratio of 14dB-20dB, the BER performance of the variable damping factor message passing method proposed in the present invention is better than that of the traditional message passing method.

[0067] It can be seen from the above embodiments that, in order to solve the problem that the damping factor in the traditional message transmission method is a fixed value, the present invention has innovatively designed a message transmission method for solving the optimal variable damping factor; by generating a damping factor at an equal interval L between 0 and 1, performing signal detection on the AFDM signal, and detecting each damping factor, according to the bit error rate obtained after the detection, taking the average bit error rate performance as the first weight and combining the average number of iterations as the second weight, the optimal damping factor that takes into account the average bit error rate BER and the average number of iterations is obtained; and then the optimal damping factor is introduced into the message transmission method to finally obtain the output signal. The present invention simultaneously meets the requirements of low complexity, high bit error rate performance and achieving global optimization.

Claims

1. A variable damping factor message transmission method based on radio frequency division multiplexing, characterized in that: The following steps are involved: Step 1: For the received AFDM demodulated signal, generate damping factors at intervals L between 0 and 1 and number them as Δ(u), u = 1, 2, ..., N L , where N L Represents the number of damping factors generated when the interval is L; Step 2: Substitute Δ(u) into the message passing method for iteration and calculate the corresponding average bit error rate BER(u) and average number of iterations Step 3: Find the minimum average bit error rate BER min ; Step 4: Find the damping factor Δ(u) that can achieve the best BER and average number of iterations; Initialize the optimal damping factor Δ(u) with the serial number minT=1, and the average number of iterations T min for Traverse Δ(u); when |BER min -BER(u)|<γ, 0<γ<1, and renew minT = u; Step 5: The optimal damping factor selected in step 4 is Δ(minT), which is substituted into the message passing method to obtain the BER.

2. The variable damping factor message transmission method based on RFDM as claimed in claim 1, characterized in that: The calculation formula of the average bit error rate BER(u) is as follows: in, The symbol represents the XOR operation, N represents the number of AFDM symbols, n iter represents the number of times the Monte Carlo method is repeated, Q represents the order of quadrature amplitude modulation (QAM), a (t) [c] and Represent the cth element of the message signal and the estimated signal respectively, t is in n iter Take the value from.

3. The variable damping factor message transmission method based on RFDM as claimed in claim 1, characterized in that: The average number of iterations The calculation formula is as follows: in Represents the number of iterations of the message passing method.

4. The variable damping factor message transmission method based on RFDM as claimed in claim 1, characterized in that: The BER min The calculation formula is:

5. The variable damping factor message transmission method based on RFDM as claimed in claim 1, characterized in that: The message transmission method is specifically as follows: Step 1: Calculate the average value of interference in the channel and variance Among them, H eff represents the channel matrix, I(d) is the index value corresponding to the non-zero element in the dth row of the channel matrix, and a j QAM Alphabet The jth element in σ 2 represents the power of the noise; Step 2: Update the probability mass function where Δ∈(0, 1] is a damping factor used to improve the performance by controlling the convergence speed, and: Where x is the message signal, y is the demodulated signal, and: Step 3: Calculate the convergence index η (i) ; Where γ is a number greater than 0 and less than 1. is an indicator function that gives the value 1 if the expression in the argument is true, and 0 otherwise, and: Where J(c) represents the index of the non-zero element in the c-th column of the channel matrix; Step 4: Update decision; If η (i) >η (i-1) , then the decision of transmitting symbols is updated as: Step 5: The message delivery method stops when at least one of the following conditions is met: (1)the (i) =1; (2) where i * ∈{1,…,i-1} is such that Maximum iteration index; (3) Reach the maximum number of iterations n iter ; Set ∈ = 0.2 to ignore small fluctuations in η.

6. The variable damping factor message transmission method based on RFDM as claimed in claim 1, characterized in that: The interval L for generating the damping factor is adjusted according to the actual application scenario; If the performance requirement is high and the computing resources are abundant, adjust it down; Otherwise, increase it.

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