CN116016061B - Short wave double-selection channel double-iteration Turbo equalization method for high maneuvering platform - Google Patents
Short wave double-selection channel double-iteration Turbo equalization method for high maneuvering platform Download PDFInfo
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Abstract
The invention belongs to the field of communication, and relates to a short wave double-selection channel double-iteration Turbo equalization method of a high maneuvering platform; the method comprises the steps of adopting a transmitter to encode, interleave and modulate an information sequence, and outputting a transmitting symbol sequence; transmitting the sending symbol sequence through a fading channel, and generating a receiving symbol sequence after adding additive Gaussian white noise; processing the received symbol sequence by using a Turbo-BEP receiver, performing internal expected propagation iteration, processing the received symbol sequence and the prior feedback signal output by external Turbo iteration by using a linear minimum mean square error equalization algorithm, and approximately outputting a posterior feedback signal by using a cavity function; through external Turbo iteration, the posterior feedback signal is demapped, deinterleaved and decoded, and a decoded information sequence is output; the information sequence is interleaved and mapped to generate an a priori feedback signal. The invention improves the performance, simultaneously effectively reduces the average iteration times and improves the real-time performance of the double iteration structure.
Description
Technical Field
The invention belongs to the field of communication, and particularly relates to a short wave double-selection channel double-iteration Turbo equalization method of a high-mobility platform.
Background
The short-wave communication is widely applied to emergency rescue scenes such as rescue and relief work by virtue of the remote communication advantages that the short-wave communication is not limited by a network hub and an active relay, and the communication emergency command network is quickly established under extreme conditions such as damage and interruption of facilities such as basic communication, electric power and the like caused by overregional, long-distance and large-range oversized disasters or war.
A short wave channel is a typical channel with a dual-choice fading characteristic, and the main propagation path is sky wave propagation, and after a signal is sent out by an antenna, the signal is reflected back to the ground through an ionosphere. Because the height and density of the ionosphere are easily affected by factors such as day and night, seasons, climate, etc., the short wave communication environment is severe, and intersymbol interference (ISI, inter-symbol interference) caused by time and frequency dispersion exists. In a single carrier system, inter-symbol interference (ISI) due to multipath delay spread may be spread to tens or even hundreds of baseband symbols, and equalization technology is an effective method for coping with channel distortion and eliminating ISI.
Soft channel equalization or probabilistic channel equalization is a technique to mitigate inter-symbol interference caused by the dispersive nature of the channel. It provides the posterior probability of the transmission symbol estimated under given observation conditions. The two tasks of equalization and decoding were initially considered separately, but adding them to a turbo equalization scheme significantly improves performance. In turbo equalization, the equalizer and decoder exchange information according to log likelihood ratios (LLRs, log Likelihood Ratio). After one or more iterations, the channel decoder generates LLRs and sends them back to the equalizer as updated a priori information.
While early Turbo equalization techniques, such as maximum a posteriori probability (MAP) detectors using BCJR estimation, can approach channel capacity under properly designed coding schemes, the computational complexity of the BCJR algorithm is proportional to the number of trellis branches M L, i.e., the complexity increases with increasing number of channel taps L and constellation size M, and the memory requirement of each BCJR step also increases with increasing number of states. The algorithm is therefore not applicable to the case of a high number of channel taps and high order modulation. An LMMSE-based equalizer [10] is subsequently proposed, which is a suboptimal alternative. To reduce this complexity, some window versions have also been developed. In particular, m.tuchler et al propose a sliding window LMMSE algorithm, l.liu et al improve these results by replacing the sliding window with an extended window. Other technicians have also proposed some approximate windowing solutions to further reduce complexity. From the Bayesian perspective, the LMMSE algorithm replaces the discrete prior distribution of the transmission symbols with Gaussian prior, so that the algorithm can obtain Gaussian posterior distribution results.
The core task of Turbo equalization is to calculate the posterior distribution of the transmitted symbol x given the observation sequence y. However, in an actual short-wave communication system, since the characteristics of the short-wave channel are complex, the posterior distribution form of the transmitted symbol is difficult to calculate, and the true value of the distribution cannot be obtained by a simple mathematical method, an approximation method is required to solve the posterior distribution.
To obtain a more accurate posterior distribution, the prior distribution may be replaced by an approximation of the probability distribution to improve the complexity of the equalization algorithm in handling higher order modulations. I.e. the joint posterior probability of the transmitted bits is approximated using an equalization algorithm based on the expected propagation (EP, expectation Propagation). The algorithm can decompose posterior distribution, and ensure that each factor after decomposition can obey specific distribution as much as possible; or assuming that a certain parameter in the posterior distribution obeys a specific distribution. The desired propagation algorithm approximates the posterior probability with a family of gaussian functions and approximates the true posterior probability by iteratively performing moment matching. In recent years, based on accurate estimation characteristics of EP algorithm, extensive researches have been made in wireless communication systems, including channel estimation, multiple-Input multiple-Output (MIMO) system receiver signal detection technology, and the like. The Expected Propagation (EP) algorithm is a deterministic approximation method, and theoretical studies show that although a deterministic approximation does not yield an accurate value of the posterior distribution, the expected propagation-based approximation method is closer to the true distribution than the LMMSE criterion-based approximation.
The idea of the EP algorithm is combined with the equalization technology and applied to the double-selection fading channel, so that the gain can be effectively improved through the double-iteration structure, but the decoding delay caused by the calculation complexity of the double-iteration structure is ignored by another problem. In the case of excessive number of iterations, although the equalization performance is guaranteed, the improvement of complexity is unacceptable.
Disclosure of Invention
Based on the problems existing in the prior art, the invention provides a short wave double-selection channel double-iteration Turbo equalization method of a high maneuvering platform, which comprises the following steps:
Adopting a transmitter to encode, interweave and modulate the information sequence to form a processed transmitting symbol sequence;
the sending symbol sequence is transmitted through a fading channel, and after additive Gaussian white noise is added, a receiving symbol sequence is generated;
Processing the received symbol sequence by using a Turbo-BEP receiver, performing internal expected propagation iteration, processing the received symbol sequence and the prior feedback signal output by external Turbo iteration by using a linear minimum mean square error (LMSE) equalization algorithm, and approximately outputting a posterior feedback signal by using a cavity function; after the posterior feedback signal is demapped, deinterleaved and decoded through external Turbo iteration, a decoded information sequence is output; the information sequence is interleaved and mapped to generate an a priori feedback signal.
Further, the transmitter is adopted to encode, interleave and modulate the information sequence, and the formation of the processed transmission symbol sequence comprises the encoding of the information sequence with a certain code rate to obtain a transmission encoding sequence; interleaving the transmission coding sequence to obtain an interleaved data sequence; and carrying out M-order modulation on the interleaved data sequence to obtain a transmitting symbol sequence.
Further, the process of the internal expected propagation iteration includes:
step 1) obtaining an initial parameter pair of a single-variable complex Gaussian product in each round of internal iteration process;
step 2) calculating a second moment of Gaussian index family approximate distribution of posterior probability of a transmitted symbol sequence according to a parameter pair in the current internal iterative process, wherein the second moment comprises a mean value and a variance;
Step 3) edge distribution and outer distribution of each symbol in the Gaussian index family approximate distribution are calculated in sequence;
step 4) estimating the second moment of the posterior probability distribution of the transmitted symbol sequence according to the symbol edge distribution and the outer distribution;
step 5) calculating the product distribution of the Gaussian index family of the outer distribution and the Gaussian index family of the next inner iterative process, enabling the product distribution to be equal to the second moment of the posterior probability distribution of the estimated transmitted symbol sequence, and calculating an intermediate parameter pair;
Step 6) based on the intermediate parameter pairs, updating the parameter pairs in the current internal iteration process by adopting damping factors, and returning to the step 2) until the internal iteration times are reached.
Further, in the step 6), the calculation formula of the intermediate parameter pair is expressed as:
Wherein, A first intermediate parameter representing the kth symbol in the 1+1st internal iteration process,/>A second intermediate parameter representing the kth symbol in the 1+1th internal iteration process,/>Representing the variance of the posterior probability distribution of the estimated transmitted symbol sequence in the first internal iteration process,/>Representing the mean value of the posterior probability distribution of the estimated transmitted symbol sequence in the first internal iteration process,/>Mean value of cavity function,/>Representing the cavity function variance.
Further, in the step 6), the formula for updating the parameter pair in the current internal iteration process by using the damping factor is expressed as:
Wherein, A first parameter representing the kth symbol during the 1+1st internal iteration, beta represents the damping factor,A first intermediate parameter representing the kth symbol in the 1+1st internal iteration process,/>A first parameter representing a kth symbol in a first internal iteration; /(I)A second parameter representing the kth symbol in the 1+1th internal iteration,/>A second intermediate parameter representing the kth symbol in the 1+1th internal iteration process,/>A second parameter representing a kth symbol in the first internal iteration.
Further, the damping factor is dynamically set, expressed as:
wherein β represents a damping factor; t represents the number of outer Turbo iterations.
Further, the external Turbo iterative process includes:
step 11), calculating posterior feedback signals of a transmitted symbol sequence in the current external iteration process by using the updated parameter pairs of the internal iteration process;
step 12) inputting the posterior feedback signal into a demapper, calculating an external log-likelihood ratio and transmitting the external log-likelihood ratio to a channel decoder;
step 13) according to each bit soft output of the channel decoder, recalculating the prior probability distribution of each transmitted symbol in the decoder, and calculating the average value and variance thereof;
step 14) returns to step 11) and enters the next round of external iteration process until the number of external iterations is reached.
The invention has the beneficial effects that:
The invention aims at a short wave fading channel of a high mobility platform, and adopts combined signal equalization and detection based on an LMMSE algorithm to process signal distortion and ISI phenomena in high frequency transmission. The algorithm is applied to a short wave fading channel, and the equalization complexity of high-order modulation is effectively reduced. While equalization performance benefits from a dual iteration structure, another problem is the excessive number of iterations. Aiming at the situation, the invention provides a double-iteration balanced Turbo method based on dynamic improvement, which ensures better performance than the traditional LMMSE algorithm. On the premise of improving the performance, the iteration times of the double-iteration structure are reduced, so that the calculation complexity is reduced. Simulation experiments show that the performance of the double-iteration Turbo equalization method based on the invention is superior to that of the traditional LMMSE algorithm, and when the number of outer layer iterations reaches 5, the performance is approximately converged. When the outer layer iteration times are fixed to be 5 times, the shortwave double-selection channel double-iteration Turbo equalization method provided by the invention can reduce the calculation complexity under the condition of almost no performance loss, and is superior to the common double-iteration equalization method.
Drawings
FIG. 1 is a flow chart of a dual iterative equalization scheme employed in an embodiment of the present invention;
FIG. 2 shows a transmitting end structure according to an embodiment of the present invention;
Fig. 3 is a Turbo receiver according to an embodiment of the present invention;
Fig. 4 shows an EP equalization receiver according to an embodiment of the present invention;
FIG. 5 is a block diagram of a dual iterative equalization architecture employed in an embodiment of the present invention
FIG. 6 is a simulation diagram of a Turbo-BEP equalization effect employed in an embodiment of the present invention;
FIG. 7 is a diagram of a three-dimensional effect of Turbo-BEP and Turbo-LMMSE equalization employed in an embodiment of the present invention;
FIG. 8 is a simulation diagram of BER curves of different inner iteration times S and outer iteration times T according to the embodiment of the present invention;
Fig. 9 is a simulation diagram of a dual iterative equalization BER curve employing a modified EP algorithm employed in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flow chart of a short-wave dual-selection channel dual-iteration Turbo equalization method of a high mobility platform according to an embodiment of the present invention, as shown in fig. 1, the method includes:
101. Adopting a transmitter to encode, interweave and modulate the information sequence to form a processed transmitting symbol sequence;
In the embodiment of the invention, the information sequence is coded with a certain code rate to obtain a transmission coding sequence; interleaving the transmission coding sequence to obtain an interleaved data sequence; and carrying out M-order modulation on the interleaved data sequence to obtain a transmitting symbol sequence.
The structural block diagram of the transmitting end adopted in the embodiment of the invention is shown in fig. 2, an information sequence a= [ a 1,…,aK]T ] composed of K information bits is coded into b= [ b 1,…,bV]T by a code rate R=K/V, the total code length after coding is V, and c= [ c 1,…,cV]T ] is obtained after interleaving. The interleaved data sequence is modulated by an M-element constellation to obtain a transmission symbol sequence x= [ x 1,…,xN]T ], wherein N represents the number of transmission symbols, and N= [ V/log 2 M ].
102. The sending symbol sequence is transmitted through a fading channel, and after additive Gaussian white noise is added, a receiving symbol sequence is generated;
In the embodiment of the present invention, the transmission symbol sequence is transmitted through a channel h= [ h 1,…,hL ] in a fourier transform form x=r (x) +ji (x) of a data frame, L represents a channel length, and each transmission symbol is represented as Wherein/>Representative/>A set of symbols of a rank constellation.
103. Processing the received symbol sequence by using a Turbo-BEP receiver, performing internal expected propagation iteration, processing the received symbol sequence and the prior feedback signal output by external Turbo iteration by using a linear minimum mean square error (LMSE) equalization algorithm, and approximately outputting a posterior feedback signal by using a cavity function; after the posterior feedback signal is demapped, deinterleaved and decoded through external Turbo iteration, a decoded information sequence is output; the information sequence is interleaved and mapped to generate an a priori feedback signal.
In an embodiment of the invention, the received signalThe discrete time expression is:
Wherein y k denotes the kth received symbol, h l denotes the ith transmission channel, x k-l+1 denotes the kth-l+1 transmitted symbols, and w k denotes the variance as The average energy per symbol and energy per bit of the transmitted signal are denoted by E s and E b, respectively. /(I)X k =0 when k <1 or k > N.
The corresponding matrix form of the formula (1) is y=hx+w, and H represents a channel matrix; w represents a noise matrix, and is specifically expressed as follows:
the key of Turbo equalization at the receiving end is that the equalizer and decoder perform iterative exchange of information on the same set of received symbols. The decoder inputs LLRL E(bt y, which is information calculated by the equalizer, i.e., information output by the equalizer for iteration during the iteration, abbreviated as L E(bt), and the decoder calculates an estimate of the information bits after one or more iterations External LLR of coded bits:
These LLRs, represented by a priori probabilities p D(xk), are remapped as shown in fig. 3 (pi and pi -1 represent the interleaving map and its inverse) and returned to the equalizer as updated a priori probabilities. This process is performed a given maximum number of iterations T until convergence.
On the basis of a traditional Turbo receiver, the posterior probability of transmitting a symbol vector x is expressed as:
wherein the indication function The value is as follows:
if the prior information is unknown to the channel decoder, then the transmitted symbols are assumed to satisfy the uniform distribution, and the prior probability thereof satisfies:
where δ (x k -x) represents the impulse function.
This assumption is used to provide the equalizer with a priori information before the Turbo receiver iterates.
In the case of a priori known channel information, the LMMSE equalizer provides a solution that approximates the posterior probability with a mean valueSum of variances/>The discrete prior probability p (x) in (3) is approximated by an independent gaussian function, the approximate distribution being expressed as:
Mu MMSE and Σ MMSE are the mean and covariance matrices of the symbol sequence, whose values are:
Initializing settings in a first iteration
In the iterative process, assuming equal probability of the symbol of the kth prior information, the out-of-symbol information calculated by (5) is passed to the channel decoder, equalizer feedback statisticsAnd/>They are obtained from the updated prior probabilities p D(xk) in the following manner:
In the technology, a desired propagation algorithm is adopted to approximate the joint posterior probability of transmission bits, and the desired propagation (EP) is a technology in Bayesian machine learning, is essentially an accurate estimation algorithm based on message passing, and approximates complex probability distribution through exponential family distribution. Given a statistical distribution containing a latent variable x and an observed variable y, a complex posterior probability distribution consisting of I non-negative factor products is:
Wherein the method comprises the steps of Index family/>Representing a set of known approximate probability functions, the set having sufficient statistics Φ (x) and obeying an exponential distribution. Because (11) contains a substance other than/>, the compositionIs not negative, t i (x), and therefore direct calculation of (11) is not feasible. Adopting an EP algorithm, and performing approximate iterative approximation on objective functions which do not belong to an exponential family according to a moment matching process, namely utilizing/>In/>Instead of t i (x), in the process, the/>, is optimizedTo obtain a precise approximation solution:
the algorithmic process of a conventional expected propagation algorithm is shown in table 1, where l represents the number of algorithm iterations.
Algorithm 1 expected propagation Algorithm
Defining i=1, … I, the expected number of propagation iterations l=1, …, S, initializing an approximate distribution factor；
1) Calculating an approximate solution q (x) from (12);
2) For a non-negative factor i=1, … I and the number of desired propagation iterations i=1, …, S, the following steps are taken:
3) Calculating probability distribution and corresponding moment:
Wherein the method comprises the steps of
4) By "moment" matching:
updating the approximate probability distribution of the next iteration
5) Ending the iterative process;
6) Output approximate probability distribution
For practical communication systems, the EP algorithm model in (11) has high complexity, and in order to apply it to the communication field, an approximation of the EP algorithm is required. The use of the approximate EP algorithm in the communication system can effectively improve the accuracy of the algorithm. In the case of a priori known channel conditions, the structure of the true distribution is preserved as much as possible, and the unknown factor t i (x) is separated from the expression of the probability distribution, the gaussian index family of the following formula is a reasonable approximation to (3):
Where the product of the indicator functions is replaced by the product of a univariate complex gaussian, each function being represented by a pair of parameters (gamma k,Λk), k=1, N. The mean and variance of q (x) are:
This solution is the Block EP (BEP) algorithm, which shows a structure similar to the LMMSE in (5).
The equalizer structure employed by the conventional algorithm to which the BEP algorithm is applied is shown in fig. 4, and q (l)(xk) is refined in the S iterative process to improve accuracy.
Based on the above analysis, the BEP equalizer in embodiments of the present invention may be further improved by using a turbo scheme. The equalizer thus produced, denoted Turbo-BEP, only needs to be replaced by the result of BEP algorithm 2 in (13) on the basis of Turbo-LMMSE. The detailed implementation of Turbo-BEP is contained in algorithm 2. The T-BEP may actually be considered as a Turbo equalizer with two loops. First, an internal iteration scheme, the BEP, is run. After S iterations of the BEP iteration process, the extrinsic LLR is provided to the decoder in the outer iterations, repeating T times. The outer distribution is approximated by a cavity function at the end of the EP algorithm. Limitations from channel coding are exploited. The output of the channel decoder is used to initialize the BEP iterative process, and then its output is fed forward to the channel decoder. This is the main difference of the previous EP scheme used in the conventional art, where the estimation is refined using only the output of the decoder, as shown in fig. 5, the dual iterative Turbo structure of the present invention consists of inner and outer iterations, which can make the proposed inner loop nonexistent.
The internal expected propagation iteration process in the embodiment of the invention comprises the following steps:
step 1) obtaining an initial parameter pair of a single-variable complex Gaussian product in each round of internal iteration process;
step 2) calculating a second moment of Gaussian index family approximate distribution of posterior probability of a transmitted symbol sequence according to a parameter pair in the current internal iterative process, wherein the second moment comprises a mean value and a variance;
Step 3) edge distribution and outer distribution of each symbol in the Gaussian index family approximate distribution are calculated in sequence;
step 4) estimating the second moment of the posterior probability distribution of the transmitted symbol sequence according to the symbol edge distribution and the outer distribution;
step 5) calculating the product distribution of the Gaussian index family of the outer distribution and the Gaussian index family of the next inner iterative process, enabling the product distribution to be equal to the second moment of the posterior probability distribution of the estimated transmitted symbol sequence, and calculating an intermediate parameter pair;
Step 6) based on the intermediate parameter pairs, updating the parameter pairs in the current internal iteration process by adopting damping factors, and returning to the step 2) until the internal iteration times are reached.
The external Turbo iteration process comprises the following steps:
step 11), calculating posterior feedback signals of a transmitted symbol sequence in the current external iteration process by using the updated parameter pairs of the internal iteration process;
step 12) inputting the posterior feedback signal into a demapper, calculating an external log-likelihood ratio and transmitting the external log-likelihood ratio to a channel decoder;
step 13) according to each bit soft output of the channel decoder, recalculating the prior probability distribution of each transmitted symbol in the decoder, and calculating the average value and variance thereof;
step 14) returns to step 11) and enters the next round of external iteration process until the number of external iterations is reached.
To further illustrate the inner desired propagation iteration and outer Turbo iteration processes of the present invention, a similar description will be provided below in connection with algorithm 2.
Algorithm 2Turbo-BEP Algorithm
Defining the outer Turbo iteration times t=1, t, the inner EP iteration times l=1, S, the symbol digits k=1, N and the initialization parameter pairs
1) For the outer Turbo iteration times, t=1, T, executing step 2);
2) Step 3) is executed for the internal EP iteration number satisfying i=1, S;
3) With initial parameter pairs The EP iteration is started and the mean and variance (second moment) of the approximation solution q (l) (x) during the first internal iteration in equation (14) and equation (15) are calculated under initial conditions.
4) Step 5) is performed for the number of sign bits to satisfy k=1, …, N;
5) Computing the kth symbol edge distribution in q (l) (x) Extrinsic distribution/>, of kth symbol
Wherein:
Mean value of cavity function,/> Representing the cavity function variance.
6) Obtain distribution ofEstimate its mean/>Sum of variances/>
7) Order theAnd/>Equal second moment (mean and variance) of the intermediate parameter pair, a calculation formula of the intermediate parameter pair can be obtained:
Wherein, A complex gaussian function first intermediate parameter representing the kth symbol in the 1 st +1 internal iteration,Complex gaussian function second intermediate parameter representing the kth symbol in the 1+1st internal iteration process,/>Representing the variance of the posterior probability distribution of the estimated kth transmitted symbol sequence in the first internal iteration process,/>Representing the mean value of the posterior probability distribution of the kth transmitted symbol sequence estimated in the first internal iteration,
8) Updating the parameter pairs with the intermediate parameter pairs, expressed as:
Wherein β represents a damping factor.
9) The loop of steps 4 to 8 ends.
10 Ending the loop of steps 2 to 9.
11 Using parameter pairs after the end of an EP iterationThe final distribution q (x) in (14) is calculated. Feedback p E(xk|y)＝q(S+1)\k(xk) is input to the demapper, and the calculated external LLRsL E(bt y) is transmitted to the channel decoder. From each bit soft output of the channel decoder, recalculate the probability distribution of each symbol p D(xk) in the decoder and calculate its average/>Sum of variancesIs given by (9) and (10).
12 Reinitializing the parameter pairs
13 Ending the loop of steps 1 to 12.
Table 3 compares in detail the complexity of the present invention (Turbo-BEP) and the Turbo-LMMSE. The Turbo-BCJR calculation complexity is also included. From a complexity point of view, as the values of M and L increase, the BCJR equalization method is not burdened, which is also consistent with the conclusions above. Where S' =s+1 and α are the complexity of the LDPC encoder.
Table 3 complexity comparison
The improved Turbo-BEP receiver and the corresponding iterative process thereof can bring excessive iterative times for a double-iterative structure, and the excessive iterative times can influence the received decoding delay.
The iteration number set by the iterative process defaults to s=10 times in EP. The damping factor β in EP iterations determines the convergence rate, and a more conservative damping factor value is typically chosen to ensure the accuracy of the algorithm.
In the conventional EP equalization algorithm, the damping factor is usually set to 0.1 and the convergence number s=10 is set to ensure accurate convergence at the EP iteration, but the convergence speed is slow. It is contemplated that the damping factor may be dynamically set to achieve lower decoding delay with reduced iteration times while ensuring some accuracy. Experiments show that in the Turbo iteration process of the previous three times, the balance performance is obviously improved, at the moment, a high damping factor can be adopted to accelerate convergence, the damping factor is dynamically reduced after three times to obtain accurate convergence, and a new damping factor value is obtained according to the thoughtThe dynamic setting of the damping factor can reduce S to 5 times within the allowable error range compared to the way the damping factor is fixed. This improved EP algorithm is called dynamic improved double iterative equalization (DI-DIE).
The channel characteristics of radio waves in short wave communication include reflection, scattering and diffraction, which cause multipath fading problems, considering the influence of topography, atmosphere and the like. The Rayleigh model is a typical multipath communication channel model. The channel is modeled by a two-path Rayleigh model, the multipath delay is 2ms, and the doppler shift is 1Hz. The channel parameter settings are in accordance with the description of the short wave channel in ITU-R, all simulations are performed with complete knowledge of the channel state information, the specific parameters being shown in table 4.
Table 4 simulation parameters
Fig. 6 is a BEP equalizer versus LMMSE equalizer BER curve for the inner EP iteration s=10, standard equalization (t=0) versus the number of iterations t=1, 3,5, 10. In the case of separation of equalization and decoding (t=0), the BEP performance of the decoder without feedback is much lower than LMMSE. After 8 iterations, the signal-to-noise ratio of the BEP algorithm is improved by about 1.7dB compared to LMMSE with ber=10 -2.5.
Fig. 7 shows a three-dimensional graph of the inner iteration number, the bit error rate and the signal to noise ratio, and it can be seen from fig. 7 that the trend of the bit error rate decreases gradually with the increase of the outer iteration number under the condition of different inner EP iteration parameters S, and reaches approximate convergence when t=5.
Fig. 8 shows BER curves for different inner and outer iteration numbers S, T for better observation of convergence. It can be observed that the equalization performance can be considered to be approximately converged when the number of internal and external iterations is 5, and the subsequent iteration performance improvement has a larger gap compared with the previous 5 iterations. As can be seen from fig. 8, by increasing the number of iterations T when s=3, there is a relatively significant gain relative to the first iteration s=1. After s=3, the gain gradually decreases. When s=10, the performance does not significantly improve over s=5 as the number of loops increases, and BEP equalization can be found to converge around the 5 th iteration. Different values of the outer iteration parameter show this trend.
If the fixed inner and outer iteration number and T are preset when performing Turbo-BEP equalization, redundant computation and unnecessary decoding delay may be caused. The method can accelerate the convergence speed of the internal EP on the premise of ensuring the performance by using the improved dynamic damping factor on the basis of the initial preset iteration times, thereby reducing the iteration times on the premise of meeting the balance performance. As shown in fig. 9, the DI-DIE algorithm has little loss of equalization performance when convergence is reached, i.e., the outer iteration number is 5 times, compared with the Turbo-BEP algorithm, and the inner iteration number S is reduced from 10 times to 5 times compared with the Turbo-BEP algorithm of the original fixed damping factor.
The invention aims at a short wave fading channel of a high mobility platform, and adopts combined signal equalization and detection based on an LMMSE algorithm to process signal distortion and ISI phenomena in high frequency transmission. The algorithm is applied to a short wave channel of a maneuvering platform, and the equalization complexity of high-order modulation is effectively reduced. While equalization performance benefits from a dual iteration structure, another problem is the excessive number of iterations. Aiming at the situation, the invention also provides a joint equalization and signal detection algorithm based on dynamic improved double iterative equalization (DI-DIE), which ensures better performance than the LMMSE algorithm. On the premise of improving the performance, the iteration times of the double-iteration structure are reduced, so that the calculation complexity is reduced. Simulation experiments show that the performance of the secondary iterative equilibrium detection algorithm based on the Turbo-BEP algorithm is superior to that of the LMMSE algorithm, and the performance approximately converges when the outer layer iteration number reaches 5 times. When the outer layer iteration number is fixed to be 5 times, the detection algorithm based on the DI-DIE algorithm provided by the invention can reduce the computational complexity by improving the inner EP iteration number under the condition of almost no performance loss, and is superior to the common double-iteration balanced detection algorithm.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: ROM, RAM, magnetic or optical disks, etc.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The utility model provides a high maneuver platform shortwave double-selection channel double-iteration Turbo equalization method which is characterized in that the method comprises the following steps:
Adopting a transmitter to encode, interweave and modulate the information sequence to form a processed transmitting symbol sequence;
the sending symbol sequence is transmitted through a fading channel, and after additive Gaussian white noise is added, a receiving symbol sequence is generated;
Processing the received symbol sequence by using a Turbo-BEP receiver, performing internal expected propagation iteration, processing the received symbol sequence and the prior feedback signal output by external Turbo iteration by using a linear minimum mean square error (LMSE) equalization algorithm, and approximately outputting a posterior feedback signal by using a cavity function; after the posterior feedback signal is demapped, deinterleaved and decoded through external Turbo iteration, a decoded information sequence is output; interleaving and mapping the information sequence to generate a priori feedback signal;
the process of the internal expected propagation iteration includes:
step 1) obtaining an initial parameter pair of a single-variable complex Gaussian product in each round of internal iteration process;
step 2) calculating a second moment of Gaussian index family approximate distribution of posterior probability of a transmitted symbol sequence according to a parameter pair in the current internal iterative process, wherein the second moment comprises a mean value and a variance;
Step 3) edge distribution and outer distribution of each symbol in the Gaussian index family approximate distribution are calculated in sequence;
step 4) estimating the second moment of the posterior probability distribution of the transmitted symbol sequence according to the symbol edge distribution and the outer distribution;
step 5) calculating the product distribution of the Gaussian index family of the outer distribution and the Gaussian index family of the next inner iterative process, enabling the product distribution to be equal to the second moment of the posterior probability distribution of the estimated transmitted symbol sequence, and calculating an intermediate parameter pair;
step 6) based on the intermediate parameter pairs, updating the parameter pairs in the current internal iteration process by adopting damping factors, and returning to the step 2) until the internal iteration times are reached;
In the step 6), the calculation formula of the intermediate parameter pair is expressed as follows:
Wherein, A first intermediate parameter representing the kth symbol in the 1+1st internal iteration process,/>A second intermediate parameter representing the kth symbol in the 1+1th internal iteration process,/>Representing the variance of the posterior probability distribution of the estimated transmitted symbol sequence in the first internal iteration process,/>Representing the mean value of the posterior probability distribution of the estimated transmitted symbol sequence in the first internal iteration process,/>Mean value of cavity function,/>Representing the cavity function variance;
in the step 6), the formula for updating the parameter pair in the current internal iteration process by adopting the damping factor is expressed as follows:
Wherein, The first parameter representing the kth symbol during the 1+1st internal iteration, β representing the damping factor,/>A first intermediate parameter representing the kth symbol in the 1+1st internal iteration process,/>A first parameter representing a kth symbol in a first internal iteration; /(I)A second parameter representing the kth symbol in the 1+1th internal iteration,/>A second intermediate parameter representing the kth symbol in the 1+1th internal iteration process,/>A second parameter representing a kth symbol in a first internal iteration;
the external Turbo iteration process comprises the following steps:
step 11), calculating posterior feedback signals of a transmitted symbol sequence in the current external iteration process by using the updated parameter pairs of the internal iteration process;
step 12) inputting the posterior feedback signal into a demapper, calculating an external log-likelihood ratio and transmitting the external log-likelihood ratio to a channel decoder;
step 13) according to each bit soft output of the channel decoder, recalculating the prior probability distribution of each transmitted symbol in the decoder, and calculating the average value and variance thereof;
step 14) returns to step 11) and enters the next round of external iteration process until the number of external iterations is reached.
2. The method for dual-iteration Turbo equalization of short-wave dual-selection channel of high mobility platform as recited in claim 1 wherein said using a transmitter to encode, interleave and modulate information sequences, forming processed transmit symbol sequences includes encoding said information sequences at a code rate to obtain transmit code sequences; interleaving the transmission coding sequence to obtain an interleaved data sequence; and carrying out M-order modulation on the interleaved data sequence to obtain a transmitting symbol sequence.
3. The high mobility platform short wave double selection channel double iteration Turbo equalization method according to claim 1, wherein the damping factor is expressed as:
wherein β represents a damping factor; t represents the number of outer Turbo iterations.
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