CN114389754A - Frequency domain self-adaptive Turbo equalization method based on FBNLMS algorithm - Google Patents
Frequency domain self-adaptive Turbo equalization method based on FBNLMS algorithm Download PDFInfo
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Abstract
The invention relates to the field of underwater acoustic communication, in particular to a frequency domain self-adaptive Turbo equalization method based on an FBNLMS algorithm. The channel equalization method comprises the following steps: preprocessing a received signal, and dividing the preprocessed signal into continuous sub-blocks through block division; accelerating filter convergence using a sliding window mechanism; the time domain receiving signal is converted into a frequency domain through FFT; multiplying the frequency domain signal by the frequency domain filter item by item to obtain a frequency domain estimation signal by an adaptive filter; performing IFFT (inverse fast Fourier transform) on the frequency domain detection data to obtain a time domain estimation signal; updating the filter by using the FBNLMS; and carrying out the next self-adaptive iterative equalization. The communication method of the invention aims at the fast time-varying underwater acoustic channel and provides a direct adaptive Turbo equalization technology to process the fast time-varying and long delay spread characteristics of the underwater acoustic channel. The convergence speed of the filter is effectively accelerated by utilizing a sliding window mechanism and an iterative equalizer structure, and the communication performance of the system is improved.
Description
Technical Field
The invention relates to the field of underwater acoustic communication, in particular to a frequency domain self-adaptive Turbo equalization method based on an FBNLMS algorithm.
Background
With the increasing demand for marine resources, people have increasingly deep exploration on oceans. Underwater communication is an indispensable technology for exploring the ocean. Underwater acoustic communication is a unique technical means for developing medium and long-distance underwater communication, and is widely concerned by people. Compared with terrestrial radio frequency communication channels, the underwater acoustic channel has the characteristics of long delay spread, remarkable time variation, serious doppler effect, limited available bandwidth and the like, and the characteristics cause great challenges for realizing high-speed underwater acoustic communication. The current technologies commonly used in underwater acoustic communication are: single carrier time domain equalization technology, single carrier frequency domain equalization technology (SC-FDE), Orthogonal Frequency Division Multiplexing (OFDM) technology and Turbo equalization technology. The OFDM has better robustness and lower complexity to a long delay spread channel, and is widely applied to high-speed underwater acoustic communication. An advantage of OFDM modulation is that symbols can be transmitted without interference in orthogonal sub-carrier channels. In time-varying channels, however, the orthogonality between subcarriers may be destroyed and inter-subcarrier interference (ICI) may exist, which may severely degrade the performance of OFDM. The advantages of SC-FDE are that it can achieve nearly identical performance with OFDM, has a low PAPR, and is insensitive to frequency offset. SC-FDE is a more potential solution to ISI and Doppler spread than OFDM.
In recent years, Turbo equalization technology has received great attention in the field of underwater acoustic communication. It can greatly improve the system performance by iteratively exchanging soft information between the decoder and the equalizer. Turbo equalization is classified into Turbo equalization based on channel estimation (CE-TEQ) and direct adaptive Turbo equalization (DA-TEQ) according to whether channel estimation is required. CE-TEQ can be implemented with robust performance and low complexity in the frequency domain in slow time-varying or time-invariant channels, but CE-TEQ performance can be severely degraded in fast time-varying channels. In contrast, DA-TEQ updates the filter coefficients in real time with an adaptive algorithm without the need for channel estimation, a good compromise between complexity and performance can be achieved. Therefore, the DA-TEQ becomes a more powerful equalization technology in the single-carrier underwater acoustic communication system.
The invention combines FBNLMS algorithm and Turbo equalization technology to be applied to underwater acoustic communication. However, due to the characteristics of long delay spread and fast time variation of the underwater acoustic channel, the performance of the traditional linear frequency domain adaptive technology is poor. Therefore, according to the characteristics of the underwater acoustic channel, the project provides a direct self-adaptive Turbo equalization technology based on frequency domain decision feedback to deal with the characteristics of long time delay expansion and fast time change of the underwater acoustic channel.
Disclosure of Invention
The invention combines FBNLMS algorithm and Turbo equalization technology to be applied to underwater acoustic communication, and provides a frequency domain direct self-adaptive Turbo equalizer suitable for a fast time-varying channel. The equalizer can effectively process the characteristics of long time delay expansion and fast time variation of the underwater acoustic channel.
The technical scheme of the invention is as follows:
a frequency domain self-adaptive Turbo equalization method based on an FBNLMS algorithm comprises the following steps:
the first step is as follows: transmitting terminal
1.1 the information bit stream is encoded by an encoder to obtain encoded bits.
And (1.2) interleaving the coded bits by a random interleaver to obtain interleaved bits.
And 1.3, carrying out QAM modulation on the interleaved bits to obtain QAM symbols.
The second step is that: and modulating the QAM symbol by using the underwater acoustic communication transmitter through a carrier wave and then transmitting.
The third step: receiving end
3.1. Preprocessing signals received by a receiving transducer, wherein the preprocessing comprises synchronization, down-conversion, sampling processing and the like;
3.2, carrying out block division on the preprocessed received signals, and dividing the preprocessed received signals into continuous sub-blocks for subsequent processing; e1+ E2
3.3 FFT transform is carried out on the time domain receiving signal, and the time domain receiving signal is converted into a frequency domain; e3
3.4 carrying out self-adaptive filtering on the received signal in the frequency domain; e4
3.5, performing IFFT (inverse fast Fourier transform) on the signals subjected to frequency domain filtering to obtain time domain detection signals; e5
3.6 updating the filter coefficient by using the FBNLMS algorithm to prepare for balancing the next sub-block; e6
3.7 initializing the filter coefficient of the current iteration by using the filter coefficient of the last iteration, and repeating for 3.3-3.6 until reaching the predefined maximum iteration times; the time domain detection signal output by the last iteration of the self-adaptive equalizer is used as the final output of the equalizer and is sent to a decoder to obtain decision bits and soft information after soft decoding; e7
3.8 the soft information is used as prior information for the next Turbo iteration after being interleaved and mapped;
the invention has the beneficial effects that:
the communication method of the invention aims at the fast time-varying underwater acoustic channel and provides a direct adaptive Turbo equalization technology to process the fast time-varying and long delay spread characteristics of the underwater acoustic channel. The convergence speed of the filter is effectively accelerated by utilizing a sliding window mechanism and an iterative equalizer structure, and the communication performance of the system is improved.
Drawings
Fig. 1 is an overall schematic view of the present invention.
Fig. 2 is a schematic diagram of an equalizer of the present invention.
Fig. 3 is a schematic diagram of a sliding window mechanism.
Fig. 4 is a channel impulse response of the simulated channel.
Fig. 5 is a graph of simulated channel scattering function.
Figure 6 is a graph of BER performance versus the effectiveness of the sliding window mechanism using simulation verification.
Figure 7 is a graph of BER performance versus the effectiveness of an iterative equalizer structure verified using simulations.
Figure 8 is a graph of BER performance versus validation of the proposed frequency domain direct adaptive Turbo equalizer.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific examples.
The present invention relates only to the channel equalization stage in a communication system and therefore focuses on the description of this part of the method, ignoring the content for the encoding, carrier modulation and decoding stages.
The DA-TEQ structure is shown in FIG. 1. Mainly comprises 7 modules: an encoder, an interleaver, a QAM modulation block, an equalizer, a deinterleaver, and a decoder.
The equalizer structure is shown in fig. 2.
The specific implementation of the equalizer comprises the following steps:
E1. dividing the time domain received signal into continuous sub-blocks;
the received data is processed in a sub-block-by-sub-block manner, with a length of NdThe received data of (2) is first divided into sub-blocks of length N. Data r input to the equalizer in processing the kth sub-blockm(k) The medicine consists of three parts:
ym(k) and
the signal expressions are respectively:
where the subscript m is the receiver index. As can be seen from (1), in the kth sub-block demodulation process, the interference of the causal part and the non-causal part is considered.
E2. Accelerating the convergence of the filter by using a sliding window mechanism;
in the conventional FBNLMS algorithm, the frequency of filter updates is kept consistent with the length of the data sub-block. In our proposed method, we propose a sliding window mechanism to separate these two parameters, as shown in fig. 3. Applying a sliding window mechanism, and in the processing process of the kth data sub-block, the input data of the feedforward filter is as follows:
similarly, the input data of the feedback filter is:
that is, the k-th process and the (k + 1) -th process have a part of data overlapping. (3) In the formula, Ns(Ns< N) is the sliding window step size.
E3. The time-frequency domain signal is converted into a frequency domain through FFT;
after FFT, the frequency domain form of the filter input signal is:
(4) where F is the normalized FFT matrix.
E4. Frequency domain adaptive filtering
By Wm(k) And b (k) represents the feedforward filter coefficient and the feedback filter coefficient of the k-th data sub-block, respectively, the frequency-domain adaptive filtering process can be represented as:
here |, indicates that the vector is multiplied symbol by symbol.
E5. Performing IFFT to obtain time domain detection data;
performing IFFT on the output of the feedforward filter and the output of the feedback filter respectively to obtain time domain filtering signals:
because at rm(k) Front N infHas been estimated in a previous process, and N of the tailbThis data is not yet estimated and is therefore discarded, leaving only the kth data sub-block. To this end, we define a block matrix T:
to summarize, after multi-pass combination, the final estimated value of the k-th data sub-block can be expressed as:
E6. and (4) updating the filter.
Conventional FBNLMS algorithms face the problem of increased steady-state MSE in non-causal environments or in situations where the filter length is insufficient. For underwater acoustic communications, the channel delay spread typically reaches several tens to several hundreds of symbol intervals, which often occurs when the filter length is insufficient. To solve this problem, an improved FBNLMS algorithm is used to solve the problem, and the improved filter update equation is:
here mufAnd mubIs the adaptive step size of the filter, and epsilon is a fixed regularization coefficient, avoiding 0 as a divisor. G is a gradient constraint matrix, which is defined to ensure a complete correspondence of the frequency domain NLMS algorithm and the time domain NLMS algorithm:
compared with the traditional FBNLMS algorithm, the improved method adds 1 group of FFT/IFFT operation.
E (k) is the frequency domain error vector whose time domain representation is:
where L istrainIndicating the training sequence length. Since only the data block, frequency, to be detected is of interestThe domain error vector is represented as:
E7. and (5) iterating the equalizer.
As mentioned earlier, adaptive frequency domain equalization requires the addition of a longer training sequence to ensure that the filter converges to a steady state. But longer training sequences reduce spectral efficiency. By employing our proposed sliding window mechanism, the convergence rate has been accelerated. Based on the inspiration of time domain data reuse technology, in order to further shorten the length of the training sequence, an iterative receiver structure is proposed to reuse data to enhance the performance. Frequency domain equalization is performed repeatedly multiple times throughout the data block. It should be noted that the iterative mechanism is employed in both the training mode and the decision-directed mode. We use
And Bi-1(K) Representing the filter coefficients after the end of the i-1 th iteration, at the start of the i-th iteration the filter vector is initialized to:
where I (I ≦ I) denotes the number of iterations of the adaptive equalizer. As the number of iterations increases, the filter stability also increases gradually and the adaptation step size should be reduced gradually. We subtract one exponential decay by decreasing the adaptation step size. Finally, the filter update equation is expressed as:
where γ (γ < 1) is a forgetting factor. The output of the last inner layer iteration is used as the final output of the filter and sent to a decoder for soft decoding, namely:
the performance of the equalizer provided by the invention is verified by using the time-varying underwater acoustic channel obtained by the underwater acoustic channel simulation software. In the simulation experiment, the underwater acoustic SIMO system has one transmitter and 2 hydrophones. The carrier frequency is 10KHz, the bandwidth is 5KHz, the communication distance is 3000m, the water depth is 50m, the distance between the transmitter and the water surface is 30m, the two receivers are respectively positioned at 24m and 28m below the water surface, and the interval is 4 m. We use the Bellhop model to generate a channel with impulse response and scattering function as shown in fig. 4 and 5, a channel delay spread of 5ms, about 25 symbol periods, and a doppler shift of about 2 Hz.
FIG. 6 shows that when NsThe variation trend of BER when varied. With NsIncreasing gradually, the BER performance decreases gradually. This is because of the smaller NsThe filter is updated more frequently, channel change can be tracked better, and therefore BER performance is improved, and the effectiveness of the sliding window mechanism provided by the user is verified. FIG. 7 shows that when IinnerThe variation trend of BER when varied. We can see that the BER performance increases with the number of inner layer iterations, which verifies the effectiveness of our proposed iterative equalizer. Fig. 8 shows BER performance of time domain Turbo equalization (TDDA-TEQ) based on NLMS algorithm and frequency domain Turbo equalization (FDCE-TEQ) based on channel estimation and our proposed frequency domain direct adaptive Turbo equalization (FDDA-TEQ) based on FBNLMS. From fig. 7 we can see that the FDDA-TEQ performance is better than the other two methods as the number of iterations increases.
Claims (7)
1. The frequency domain self-adaptive Turbo equalization method based on the FBNLMS algorithm is characterized by comprising the following steps of:
the first step is as follows: transmitting terminal
1.1, coding an information bit stream by a coder to obtain coded bits;
1.2 interleaving the coded bits by a random interleaver to obtain interleaved bits;
1.3, carrying out QAM modulation on the interleaved bits to obtain QAM symbols;
the second step is that: modulating a QAM symbol by using an underwater acoustic communication transmitter through a carrier wave and then transmitting;
the third step: receiving end
3.1. Preprocessing signals received by a receiving transducer;
3.2, carrying out block division on the preprocessed received signals, and dividing the preprocessed received signals into continuous sub-blocks for subsequent processing;
3.3 FFT transform is carried out on the time domain receiving signal, and the time domain receiving signal is converted into a frequency domain;
3.4 carrying out self-adaptive filtering on the received signal in the frequency domain;
3.5, performing IFFT (inverse fast Fourier transform) on the signals subjected to frequency domain filtering to obtain time domain detection signals;
3.6 updating the filter coefficient by using the FBNLMS algorithm to prepare for balancing the next sub-block;
3.7 initializing the filter coefficient of the current iteration by using the filter coefficient of the last iteration, and repeating the steps 3.3-3.6 until reaching the predefined maximum iteration times; the time domain detection signal output by the last iteration of the self-adaptive equalizer is used as the final output of the equalizer and is sent to a decoder to obtain decision bits and soft information after soft decoding;
and 3.8, the soft information is used as prior information for the next Turbo iteration after interleaving and mapping.
2. The frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 1, wherein the step 3.2 specifically operates as follows:
3.2.1 dividing the time domain received signal into continuous sub-blocks;
the received data is processed in a sub-block-by-sub-block manner, with a length of NdThe received data of (a) is first divided into subblocks of length N; data r input to the equalizer in processing the kth sub-blockm(k) The medicine consists of three parts:
ym(k) and
the signal expressions are respectively:
wherein the subscript m is the receiver index;
3.2.2 accelerating the convergence of the filter by using a sliding window mechanism;
applying a sliding window mechanism, and in the processing process of the kth data sub-block, the input data of the feedforward filter is as follows:
similarly, the input data of the feedback filter is:
that is, the k processing and the (k + 1) processing have a part of data overlapped; (3) in the formula, Ns(Ns< N) is the sliding window step size.
3. The frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 2, wherein the step 3.3 specifically operates as follows:
the time-frequency domain signal is converted into a frequency domain through FFT; after FFT, the frequency domain form of the filter input signal is:
(4) where F is the normalized FFT matrix.
4. The frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 3, wherein the step 3.4 specifically operates as follows:
by Wm(k) And b (k) represents the feedforward filter coefficient and the feedback filter coefficient of the k-th data sub-block, respectively, the frequency-domain adaptive filtering process can be represented as:
here |, indicates that the vector is multiplied symbol by symbol.
5. The frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 4, wherein the step 3.5 specifically operates as follows:
performing IFFT on the output of the feedforward filter and the output of the feedback filter respectively to obtain time domain filtering signals:
because at rm(k) Front N infHas been estimated in a previous process, and N of the tailbThe data is not estimated yet, so the data is discarded, and only the kth data sub-block is reserved; to this end, a block matrix T is defined:
after multi-channel combination, the final estimated value of the k-th data sub-block is expressed as:
6. the frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 5, wherein the step 3.6 specifically operates as follows:
the improved filter update equation is:
μfand mubThe self-adaptive step length of the filter is epsilon, a fixed regularization coefficient is epsilon, and 0 is avoided as a divisor; g is a gradient constraint matrix, which is defined to ensure a complete correspondence of the frequency domain NLMS algorithm and the time domain NLMS algorithm:
compared with the traditional FBNLMS algorithm, the improved method adds 1 group of FFT/IFFT operation;
e (k) is the frequency domain error vector whose time domain representation is:
where L istrainRepresents the training sequence length; because only the data block currently to be detected is of interest, the frequency domain error vector is represented as:
7. the frequency domain adaptive Turbo equalization method based on the FBNLMS algorithm according to claim 6, wherein the step 3.7 specifically operates as follows:
by using
And Bi-1(K) Representing the filter coefficients after the end of the i-1 th iteration, at the start of the i-th iteration the filter vector is initialized to:
i (I ≦ I) represents the adaptive equalizer iteration number;
the filter update equation is expressed as:
gamma (gamma < 1) is a forgetting factor; and taking the output of the last inner layer iteration as the final output of the filter, and sending the final output to a decoder for soft decoding, namely:
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