CN113746765B - UDDF underwater acoustic communication cooperation method and multi-branch merging and equalization frequency domain joint implementation method - Google Patents

UDDF underwater acoustic communication cooperation method and multi-branch merging and equalization frequency domain joint implementation method Download PDF

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CN113746765B
CN113746765B CN202010468904.2A CN202010468904A CN113746765B CN 113746765 B CN113746765 B CN 113746765B CN 202010468904 A CN202010468904 A CN 202010468904A CN 113746765 B CN113746765 B CN 113746765B
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CN113746765A (en
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刘志勇
谭周美
柯淼
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Harbin Institute of Technology Weihai
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03159—Arrangements for removing intersymbol interference operating in the frequency domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B11/00—Transmission systems employing sonic, ultrasonic or infrasonic waves
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
    • H04B13/02—Transmission systems in which the medium consists of the earth or a large mass of water thereon, e.g. earth telegraphy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/155—Ground-based stations
    • H04B7/15564—Relay station antennae loop interference reduction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a underwater sound cooperation communication system based on frequency domain processing, in particular to a UDDF underwater sound communication cooperation strategy and a multi-branch merging and balancing frequency domain joint implementation method, wherein an asynchronous underwater decoding and distributed frequency forwarding underwater sound communication cooperation strategy is firstly provided, in the UDDF underwater sound communication cooperation strategy, as carrier frequencies used by a source node and a relay node are different, a destination node can distinguish signals from the source node and the relay node in a frequency domain, and IDI between the source node and the relay node can be eliminated theoretically; then, based on the UDDF underwater acoustic communication cooperation strategy, a self-adaptive multi-branch combining and balancing frequency domain joint implementation method is provided.

Description

UDDF underwater acoustic communication cooperation method and multi-branch merging and equalization frequency domain joint implementation method
Technical field:
the invention relates to a water sound cooperation communication system based on frequency domain processing, in particular to a UDDF water sound communication cooperation method and a multi-branch combination and balance frequency domain combination realization method which enable a destination node to distinguish signals from a source node and a relay node in frequency so as to eliminate IDI between the source node and the relay node.
The background technology is as follows:
the underwater acoustic channel (UAC, underwater acoustic channel) is one of the most complex wireless channels to date. The signal transmission in UACs has the characteristics of long propagation delay, limited bandwidth and large multipath delay spread, which presents challenges for achieving a reliable underwater acoustic communication link.
In order to improve the reliability of the underwater acoustic communication link, the cooperative communication technology that has appeared in recent years brings a trigger to solve the problem. In cooperative communication, a signal of a source node is transmitted to a target node with the aid of a relay node. Thus, the destination node receives a plurality of signals from the direct path and the relay path. The combining of multiple signals will affect the performance of cooperative communication to some extent. In the research of underwater acoustic cooperative communication, the existing scheme mostly adopts equal gain combination (EGC, equal gain combining) and maximum ratio combination (MRC, maximum ratio combining). MRC may obtain better performance than EGC, but it needs to assume that the channel state information (CSI, channel state information) between nodes is known. CSI is difficult to obtain for practical underwater acoustic channels. In view of the long and variable propagation delay in UACs, asynchronous underwater amplification forwarding (UAF, underwater amplify-and-forward) and underwater decoding forwarding (UDF, underwater decode-and-forward) have been proposed in the prior art, which assumes that Inter-symbol interference (ISI, inter-symbol interference) and Inter-packet interference (IDI, inter data-packet interference) are absent in the received signals from the source node and the relay node. For both modes of collaboration, the relay node immediately forwards the received signal from the source node to the destination node after processing it, rather than waiting for the next time slot to retransmit as in the case of Amplify-and-forward (AF) and Decode-and-forward (DF) in terrestrial wireless communications. However, due to varying propagation delays of the UAC and large multipath delay spread, the presence of IDI and ISI is unavoidable. To cancel ISI, time domain or frequency domain equalization has been used in underwater acoustic communications. The multipath delay spread time of the underwater sound channel is longer, and the time domain equalization needs longer tap length to obtain better performance, so that the implementation complexity is high. Frequency Domain Equalization (FDE), frequency-domain equalization, can effectively reduce implementation complexity. In the prior art, however, research is directed to non-cooperative communication systems, which means that no relay is involved in the transmission of information. To our knowledge, there is a lack of research on frequency domain equalization for underwater acoustic cooperative communication.
The invention comprises the following steps:
aiming at the defects and shortcomings existing in the prior art, the invention provides an underwater sound communication cooperation algorithm of asynchronous underwater decoding and distributed frequency forwarding (UDDF, underwater decode and distributed-frequency forward), in the UDDF underwater sound communication cooperation algorithm, as carrier frequencies used by a source node and a relay node are different, a destination node can distinguish signals from the source node and the relay node in a frequency domain, so that IDI between the source node and the relay node can be eliminated theoretically; then, based on UDDF underwater acoustic communication cooperation algorithm, a Frequency domain combined multi-branch merging and balancing detection method (FD-JMCED, frequency-domain joint multi-branch combining and equalization detector) is provided.
The invention is achieved by the following measures:
a UDDF underwater sound communication cooperation method is applied to an asynchronous underwater sound cooperation communication system, the system is composed of a source node S, a relay node R and a destination node D, each node works in a half duplex mode and is provided with a single sending and receiving unit, all data packets received by the relay node and the destination node are assumed to be completely synchronous, and in addition, all nodes are assumed to have the same average power constraint; assuming that the relay node is located between the source node and the destination node, the distances of S-D, S-R and R-D are respectively defined by l SD 、l SR And l RD Indicating that l should be satisfied SD ＞l SR And l SD ＞l RD In order to prevent as much as possible the problem of erroneous transmission of the relay node, let l be SR Less than l RD So that the S-R channel has a sufficient instantaneous signal to noise ratio; the underwater acoustic communication cooperative algorithm is characterized by comprising a broadcasting stage and a relay stage:
in the broadcasting stage, a data packet is broadcast to a relay node and a destination node by a source node, and a signal continuously transmitted by the source node is expressed as
Where d (j) represents equal rate uncorrelated Binary Phase Shift Keying (BPSK) data, d (j) ε { +1, -1}, A j Representing the amplitude of the transmitted signal, T is the symbol duration, q (T) represents a raised cosine pulse, which is a real function, limited to [0, T at time intervals]And is normalized, i.eω cS Representing the carrier angular frequency;
under the assumption of ideal carrier and synchronization, after passing through the carrier removal, waveform shaping and low pass filters, the received baseband signal recovered by the relay node is given by
Wherein P is S Is the transmitting power of the source node, h SR (t) represents the impulse response of the underwater acoustic channel from the source node to the relay node, x (t) may be represented by x (t) =f -1 {|Q(f)| 2 Obtained, F -1 {.cndot } represents the inverse fourier transform, Q (F) =f { Q (t) }, F {.cndot.cndot.cndot.c. }, represents the fourier transform, n SR (t) represents a noise signal at a relay node;
the corresponding baseband received signal at the destination node may be expressed as
Wherein h is SD (t) represents the impulse response of the underwater acoustic channel from the relay to the destination node, n SD (t) represents a noise signal at the destination node;
in the relay phase, the received signal r is first received SR (t) decoding and then using the decoded information bits other than ω cS Is of carrier frequency omega cR Modulated and immediately generated signal s R (t) retransmission to destination node, s R (t) can be represented by the following formula
Wherein the method comprises the steps ofRepresenting the decoded information bits, ω cR Is the carrier angular frequency used for relay forwarding, for s R (t) the baseband signal received by the destination node can be represented in a form similar to that of equation (3)
Wherein P is R Representing the transmit power of the relay node, n RD And (t) represents a noise signal at the destination node.
The principle of the UDDF underwater acoustic communication cooperative algorithm provided by the invention is that the carrier frequencies used by the source node and the relay node in forwarding are different, and in addition, the band-limited waveform is used so as to eliminate the use of different carrier frequencies (omega) cS And omega cR ) The received signals are overlapped, from the point of view of the destination node, the signals received from the source node and the relay node work in different frequency bands, and the received signals from the source node and the relay node can be distinguished and extracted according to the different frequency bands, so that IDI can be removed to a certain extent through band-pass filtering; furthermore, since signals from the source node or the relay node can be separated by different frequency bands, the distance requirements between the source node, the relay node and the destination node are not as strict as those of UDF, and there are corresponding restrictions to avoid ISI and IDI; in the present invention, even if signals received from the source node and the relay node are completely overlapped, a desired signal can be extracted, and thus, the end-to-end delay can be further reduced.
The invention also provides a multi-branch merging and equalizing frequency domain joint implementation method based on the UDDF underwater acoustic communication cooperation method, which is characterized in that the symbol interval T is used for r SD (t) and r RD (t) sampling corresponding to r SD (t) and r RD The discrete received signals of (t) are respectively represented by a SD (n) and a RD (n) representing that the input block data of each branch is composed of 2M sampling values including M previous samples and M current samples, according to the overlap storage method, by a i (n) the resulting signal vector is as follows
Where i ε { SD, RD }, [ a ] i (nM),…,a i (nM+M-1)]Representing the nth data block, M is the length of the block for joint processingThe received signals of the two branches, the total signal vector, can be given by:
a(n)＝[a SD (n)；a RD (n)](7) Wherein a (n) is a 4M'1 vector;
the frequency domain signal vector form of a (n) is calculated using a 4M point Fast Fourier Transform (FFT), which can be derived from the following equation
A(n)＝diag{FFT[a(n)]} (8)
Where A (n) represents a 4M'4M diagonal matrix and diag is an operator used to calculate the diagonal matrix. The corresponding frequency domain tap weight vector can be calculated by the following formula
Where W (n) is a frequency domain 4M'1 weight vector, W i (n) is a time-domain M'1 weight vector, i ε { SD, RD },0 M×1 Representing the M'1 null vector, the frequency domain output vector is therefore given by:
Y(n)＝A(n)W(n) (10)
the 2M time domain outputs may be represented as
y(n)＝[q out (M+1:2M)；q out (3M+1:4M)] (11)
Wherein q is out (n) =ifft { Y (n) }, IFFT represents an Inverse Fast Fourier Transform (IFFT), retaining only elements from m+1 to 2M and from 3m+1 to 4m+1, because q out (M+1:2M) and q Out (3M+1:4M) represents the same transmission information d (n), so the total output vector of the detection method is represented by q out (M+1:2M) and q Out The sum of (3M+1:4M) gives
y total (n)＝q out (M+1:2M)+q out (3M+1:4M) (12)
Wherein y is total (n) is a time domain M'1 vector, and for the nth data block, the total error vector can be calculated by the following formula:
the purpose of the detection method of the present invention is to recover the information bits sent by the source node, so that the frequency domain weight vectors are selected with minimum mean square error (MSE, mean square error) according to Minimum Mean Square Error (MMSE) criteria
By adopting the iterative process of the random algorithm, the realization of MSE minimization can be simplified, the total error vector is used for self-adaptively adjusting the frequency domain weight vector, and in order to update the weight vectors of SD and RD branches, the frequency domain error vector is calculated by the following formula:
in order to solve the gradient noise amplification problem caused when the element value in A (n) is large, a normalization method similar to that in a time domain normalized least mean square (NLMS, normalized least mean square) algorithm is adopted, and the normalization coefficient is calculated according to each weight in the weight vector as follows
Wherein A is ii (n) represents the elements of the ith row and ith column on the diagonal of matrix A (n), l is a constant, l>0,l for overcoming A ii (n) numerical computation difficulties in transient small hours; for convenience of representation, 4M normalized coefficients are represented in matrix form
G(n)＝diag{[g 1 ,g 2 ,…,g 4M ]} (17)
Thus, the block gradient estimate is given by:
wherein p (n) =ifft [ G (n) a H (n)E(n)]. With these definitions, adaptationThe frequency domain joint multi-branch combining equalization algorithm can be expressed as
Where m represents the step size parameter.
In order to meet the assumption that the channel is unchanged during the transmission of a data packet, the invention adopts a short data packet which consists of a training sequence and data symbols, the number of symbols of a steady observation training sequence used for weight vector adjustment is limited, in order to ensure the convergence of the proposed adaptive algorithm (19), the training sequence in the data packet is repeatedly reused to update the weight vector until the weight vector converges to a steady state, and the weight vector is initialized by the weight after the last update for each repeated update using the same training sequence; in addition, multi-branch combining does not need to assume that CSI between nodes is known, but is derived based on iterative updating of the proposed adaptive algorithm, so that the algorithm is more suitable for practical underwater acoustic communication systems.
Description of the drawings:
fig. 1 is a schematic diagram of a frequency joint multi-branch combining equalization detection method in the present invention.
Fig. 2 is a graph showing the bit error rate performance in example 1 of the present invention.
FIG. 3 is a graph showing convergence performance in example 1 of the present invention.
The specific embodiment is as follows:
the invention will be further described with reference to the drawings and examples.
The invention firstly proposes an asynchronous underwater decoding and distributed frequency forwarding (UDDF, underwater decode and distributed-frequency forward) mode, in the UDDF, the destination node can distinguish signals from the source node and the relay node in a frequency domain due to different carrier frequencies used by the source node and the relay node, so that IDI between the source node and the relay node can be eliminated theoretically. Then, for the UDDF cooperative mode, a Frequency domain combined multi-branch combining and balancing detection method (FD-JMCED, frequency-domain joint multi-branch combining and equalization detector) is provided, the combination of the received signals from the source node and the relay node can be obtained based on the proposed adaptive algorithm, the algorithm does not need to assume that CSI between links is known, and the algorithm also jointly realizes balancing while combining.
The invention is applied to an asynchronous underwater acoustic cooperative communication system which consists of a source node S, a UDDF relay node R and a destination node D, wherein each node works in a half duplex mode and is provided with a single sending and receiving unit. It is assumed that all packets received by the relay node and the destination node are completely synchronized. Further, it is also assumed that all nodes have the same average power constraint.
In the proposed UDDF mode, the distances of S-D, S-R and R-D are respectively defined by l, assuming that the relay node is located between the source node and the destination node SD 、l SR And l RD Indicating that l should be satisfied SD ＞l SR And l SD ＞l RD Is a condition of (2). In order to prevent as far as possible the problem of erroneous transmission of relay nodes, let l be SR Less than l RD So that the S-R channel has a sufficient instantaneous Signal-to-noise ratio (SNR). In the cooperative transmission process, the implementation of the UDDF mode needs two phases, namely a broadcast phase and a relay phase, wherein in the broadcast phase: the data packets are broadcast from the source node to the relay node and the destination node, and the signal continuously transmitted by the source node can be expressed as
Where d (j) represents equal rate uncorrelated Binary Phase Shift Keying (BPSK) data, d (j) ε { +1, -1}, A j Representing the amplitude of the transmitted signal, T is the symbol duration, q (T) represents a raised cosine pulse, which is a real function, limited to [0, T at time intervals]And is normalized, i.eω cS Representing carrier wavesAngular frequency.
Under the assumption of ideal carrier and synchronization, after passing through the carrier removal, waveform shaping and low pass filters, the received baseband signal recovered by the relay node is given by
Wherein P is S Is the transmitting power of the source node, h SR (t) represents the impulse response of the underwater acoustic channel from the source node to the relay node, x (t) may be represented by x (t) =f -1 {|Q(f)| 2 Obtained, F -1 {.cndot } represents the inverse fourier transform, Q (F) =f { Q (t) }, F {.cndot.cndot.cndot.c. }, represents the fourier transform, n SR (t) represents a noise signal at the relay node.
The corresponding baseband received signal at the destination node may be expressed as
Wherein h is SD (t) represents the impulse response of the underwater acoustic channel from the relay to the destination node, n SD And (t) represents a noise signal at the destination node.
For UDDF relay, first the received signal r SR (t) decoding. The decoded information bits are then used with a different value than ω cS Is of carrier frequency omega cR Modulated and immediately generated signal s R (t) retransmission to destination node, s R (t) can be represented by the following formula
Wherein the method comprises the steps ofRepresenting the decoded information bits, ω cR Is the carrier angular frequency used for relay forwarding. For s R (t) received by destination nodeThe baseband signal may be represented in a form similar to equation (3)
Wherein P is R Representing the transmit power of the relay node, n RD And (t) represents a noise signal at the destination node.
The principle of UDDF is that the carrier frequencies used by the source node and the relay node for forwarding are different. Furthermore, band-limited waveforms are used to cancel the use of different carrier frequencies (ω cS And omega cR ) Overlap of received signals. From the point of view of the destination node, the signals received from the source node and the relay node operate in different frequency bands. The received signals from the source node and the relay node can be discriminated and extracted according to the difference of frequency bands, so that IDI can be removed to some extent by band pass filtering. Further, since signals from the source node or the relay node can be separated by different frequency bands, the distance requirements between the source node, the relay node and the destination node are not as strict as those of UDF, which has a corresponding limitation condition in order to avoid ISI and IDI. In the proposed UDDF, the desired signal can be extracted even if the signals received from the source node and the relay node completely overlap, and thus the end-to-end delay can be further reduced.
The invention also provides an FD-JMCED detection method based on the UDDF underwater acoustic communication cooperative algorithm, the detection method has a schematic block diagram shown in figure 1, and the method is different from a traditional detector (single-branch Frequency domain equalization detector) in point-to-point communication, in the FD-JMCED, the branch number is more than 1, in order to obtain better diversity gain, the input signals of two branches are processed in a combined way, the invention combines the Frequency domain equalization and the multi-branch combination to be realized, the Frequency domain equalization and the multi-branch combination are fused into an effective detector, and particularly, when the branch number is 1, the FD-JMCED is degraded into a Frequency domain single-branch equalization detector (FD-SBED, frequency-domain single branch equalization detector).
At symbol interval T to r SD (t) and r RD (t) after sampling, corresponds to r SD (t) and r RD The discrete received signals of (t) may be respectively represented by a SD (n) and a RD (n) represents a compound. In FD-JMCED, according to the overlap storage method, the input block data of each branch consists of 2M sampling values, including M previous samples and M current samples, consisting of a i (n) the resulting signal vector is as follows
Where i ε { SD, RD }, [ a ] i (nM),…,a i (nM+M-1)]Represents the nth data block, M is the length of the block. For joint processing of the received signals of the two branches, the total signal vector can be given by:
a(n)＝[a SD (n)；a RD (n)](7) Wherein a (n) is a 4M'1 vector.
Calculating a frequency domain signal vector form of a (n) using a 4M point Fast Fourier Transform (FFT), where a (n) represents a 4M'4M diagonal matrix, diag being an operator for calculating the diagonal matrix, can be derived from a (n) =diag { FFT [ a (n) ] } 8;
the corresponding frequency domain tap weight vector can be calculated by the following formula
Where W (n) is a frequency domain 4M'1 weight vector, W i (n) is a time-domain M'1 weight vector, i ε { SD, RD },0 M×1 Representing the M'1 null vector, the frequency domain output vector of FD-JMCED can therefore be given by:
Y(n)＝A(n)W(n) (10)
the 2M time domain outputs of FD-JMED can be expressed as
y(n)＝[q out (M+1:2M)；q out (3M+1:4M)] (11)
Wherein q is out (n) =ifft { Y (n) }, IFFT represents an Inverse Fast Fourier Transform (IFFT). Only elements from m+1 to 2M and from 3m+1 to 4m+1 remain. Because q out (M+1:2M) and q Out (3M+1:4M) represents the sameD (n), so that the total output vector of the detector can be defined by q out (M+1:2M) and q Out The sum of (3M+1:4M) gives
y total (n)＝q out (M+1:2M)+q out (3M+1:4M) (12)
Wherein y is total (n) is the time domain M'1 vector. For the nth data block, the total error vector may be calculated by the following formula:
the purpose of the detector is to recover the information bits sent by the source node. Thus, the frequency domain weight vectors are selected to minimize the mean square error (MSE, mean square error) according to a Minimum Mean Square Error (MMSE) criterion
By employing an iterative process of a random algorithm, the implementation of MSE minimization may be simplified. The total error vector may be used to adaptively adjust the frequency domain weight vector. To update the weight vectors of the SD and RD branches, the frequency domain error vector can be calculated by:
in order to solve the gradient noise amplification problem caused when the element value in a (n) is large, a normalization method similar to that in the time domain normalized least mean square (NLMS, normalized least mean square) algorithm is adopted. The normalized coefficient is calculated as follows for each weight in the weight vector
Wherein A is ii (n) represents the elements of the ith row and ith column on the diagonal of matrix A (n), l is a constantNumber, l>0,l for overcoming A ii (n) numerical computation at small transients is difficult. For convenience of representation, 4M normalized coefficients may be represented in a matrix form
G(n)＝diag{[g 1 ,g 2 ,…,g 4M ]} (17)
Thus, the block gradient estimate can be given by:
wherein p (n) =ifft [ G (n) a H (n)E(n)]. With these definitions, the adaptive frequency domain joint multi-branch combining equalization algorithm can be expressed as
Where m represents the step size parameter.
To meet the assumption that the channel is unchanged during one packet transmission, we use a short packet, which consists of a training sequence and data symbols. The number of stationary observation training sequence symbols used for weight vector adjustment is limited. To ensure convergence of the proposed adaptive algorithm (19), the training sequences in the data packets are repeatedly reused to update the weight vectors until the weight vectors converge to a steady state. For each repeated update using the same training sequence, the weight vector is initialized with the last updated weight. In addition, it should be noted that multi-branch combining does not require that the CSI between nodes be assumed to be known, but is derived based on iterative updates of the proposed adaptive algorithm. Therefore, the algorithm is more suitable for an actual underwater sound communication system.
Example 1:
in this example, a Monte Carlo simulation was built based on the underwater acoustic channel model. In this model, the carrier frequencies of the source node and the relay node are set to 10kHz and 20kHz, respectively. The water depth was set to 80m, and the source node, relay node and destination node were located 50m,30m and 20m from the sea surface, respectively. Assume that the distances S-D, S-R and R-D are 1000m,350m and 700m, respectively. We also assume that the underwater acoustic channel is semi-stationary, meaning that the channel remains unchanged during the transmission of one packet, but will change for the next packet. In the simulation, BPSK modulation was used. The weight vector lengths of both the S-D and R-D branches are set to 32.m and l are set to 0.15 and 0.5, respectively. The data frame is composed of K sets of training sequences and data symbols, and in each data packet, the length of the training sequence is set to 256, and the length of the data symbol is also set to 256. The number of times of repeated use of the training sequence is set to 4. The data packets in the data frame are transmitted consecutively, so we assume that the signals received from the source node and the relay node overlap entirely.
In FIG. 2, we examine the Bit Error Rate (BER) performance of the proposed FD-JMCED and the existing method, with the horizontal axis representing the signal-to-noise ratio (SNR) SNR of the S-D branch SD . In the simulation, it is assumed that the conditional SNR is satisfied RD ＝SNR SD +1.FD-SMCED refers to an equalization detector similar to that under a single branch that will update the weight vector for each branch independently and then combine the outputs of the S-D and R-D branches by equal gain combining. As can be seen from fig. 2, FD-SMCED can achieve better BER performance than FD-SBED. This is because the balanced outputs of the S-D and R-D branches are combined in EGC mode, and a certain diversity gain is obtained. Furthermore, as can be seen from fig. 2, the proposed FD-JMCED achieves a comparable gain compared to FD-SMCED. This is because in FD-JMCED, the weight vectors of the S-D and R-D branches are obtained in combination from the total error in (13), so that a better combining gain can be obtained.
FIG. 3 shows convergence properties of FD-JMED and FD-SBED. Since the training sequence in each packet was reused 4 times in the simulation, the length of the horizontal axis is 1024. It can be seen from fig. 3 that similar convergence rates are achieved for FD-JMCED and FD-SBED, but better steady-state mean square error performance is achieved for FD-JMCED. This result is consistent with the comparison of BER performance in fig. 2, because steady state MSE performance is an important parameter that affects equalization performance.
The simulation results verify the effectiveness of the method and demonstrate that the invention has advantages over existing methods.

Claims (4)

1. A UDDF underwater acoustic communication cooperation method is applied to an asynchronous underwater acoustic cooperation communication system, the system is composed of a source node S, a relay node R and a destination node D, each node works in a half duplex mode and is provided with a single sending and receiving unit, all data packets received by the relay node and the destination node are assumed to be completely synchronous, and all nodes are assumed to have the same average power constraint; assuming that the relay node is located between the source node and the destination node, the distances of S-D, S-R and R-D are respectively defined by l SD 、l SR And l RD Indicating that l should be satisfied SD ＞l SR And l SD ＞l RD Under the condition of 1 SR Less than l RD The underwater acoustic communication cooperation method is characterized by comprising a broadcasting stage and a relay stage:
in the broadcasting stage, a data packet is broadcast to a relay node and a destination node by a source node, and signals continuously transmitted by the source node are expressed as follows:
where d (j) represents equal rate uncorrelated binary phase shift keying data, d (j) ∈ { +1, -1}, A j Representing the amplitude of the transmitted signal, T being the symbol duration; q (t) represents a raised cosine pulse, which is a real function, limited to [0, T ] at time intervals]And is normalized, i.eω cS Representing the carrier angular frequency;
under the assumption of ideal carrier and synchronization, after passing through the carrier removal, waveform shaping and low pass filters, the received baseband signal recovered by the relay node is given by:
wherein P is S Is the transmitting power of the source node, h SR (t) represents the impulse response of the underwater acoustic channel from the source node to the relay node, x (t) is represented by x (t) =f -1 {|Q(f)| 2 Obtained, F -1 {.cndot } represents the inverse fourier transform, Q (F) =f { Q (t) }, F {.cndot.cndot.cndot.c. }, represents the fourier transform, n SR (t) represents a noise signal at a relay node;
the corresponding baseband received signal at the destination node is represented as:
wherein h is SD (t) represents the impulse response of the underwater acoustic channel from the relay to the destination node, n SD (t) represents a noise signal at the destination node;
in the relay phase, the received signal r is first received SR (t) decoding and then using the decoded information bits other than ω cS Is of carrier frequency omega cR Modulated and immediately generated signal s R (t) retransmission to destination node, s R (t) is represented by the following formula:
wherein the method comprises the steps ofRepresenting the decoded information bits, ω cR Is the carrier angular frequency used for relay forwarding, for s R (t) the baseband signal received by the destination node is as follows:
wherein P is R Representing the transmit power of the relay node, n RD And (t) represents a noise signal at the destination node.
2. A multi-branch combining and equalizing frequency domain joint implementation method based on the UDDF underwater acoustic communication cooperation method as claimed in claim 1, characterized in that r is paired with a symbol interval T SD (t) and r RD (t) sampling corresponding to r SD (t) and r RD The discrete received signals of (t) are respectively represented by a SD (n) and a RD (n) representing that the input block data of each branch is composed of 2M sampling values including M previous samples and M current samples, according to the overlap storage method, by a i (n) the resulting signal vector is as follows
Where i ε { SD, RD }, [ a ] i (nM),…,a i (nM+M-1)]Representing the nth data block, M is the length of the block, for joint processing of the received signals of the two branches, the total signal vector is given by:
a(n)＝[a SD (n)；a RD (n)] (7)，
wherein a (n) is a 4M'1 vector;
calculating a frequency domain signal vector form of a (n) using a 4M point fast Fourier transform, derived from the following equation
A(n)＝diag{FFT[a(n)]} (8)
Wherein A (n) represents a 4M'4M diagonal matrix, diag is an operator used to calculate the diagonal matrix, and the corresponding frequency domain tap weight vector can be calculated by
Where W (n) is a frequency domain 4M'1 weight vector, W i (n) is a time-domain M'1 weight vector, i ε { SD, RD },0 M×1 Representing the M'1 null vector, the frequency domain output vector is therefore given by:
Y(n)＝A(n)W(n) (10)
the 2M time domain outputs are represented as
y(n)＝[q out (M+1:2M)；q out (3M+1:4M)] (11)
Wherein q is out (n) =ifft { Y (n) }, IFFT represents an inverse fast fourier transform, retaining only elements from m+1 to 2M and from 3m+1 to 4m+1, because q out (M+1:2M) and q Out (3M+1:4M) represents the same transmission information d (n), so the total output vector of the detection method is represented by q out (M+1:2M) and q Out The sum of (3M+1:4M) gives:
y total (n)＝q out (M+1:2M)+q out (3M+1:4M) (12)
wherein y is total (n) is a time domain M'1 vector, and for the nth data block, the total error vector is calculated by the following formula:
3. the method of claim 2, wherein the frequency domain weight vectors are selected to minimize mean square error based on a minimum mean square error criterion:
by adopting an iterative process of a random algorithm, the implementation of MSE minimization is simplified, the total error vector is used for adaptively adjusting the frequency domain weight vector, and in order to update the weight vectors of SD and RD branches, the frequency domain error vector is calculated by the following formula:
in order to solve the gradient noise amplification problem caused when the element value in A (n) is large, a normalization method similar to that in a time domain normalization least mean square algorithm is adopted, and the normalization coefficient is calculated according to each weight in the weight vector as follows
Wherein A is ii (n) represents the elements of the ith row and ith column on the diagonal of matrix A (n), l is a constant, l>0,l for overcoming A ii (n) numerical computation difficulties in transient small hours; for convenience of representation, 4M normalized coefficients are represented in matrix form
G(n)＝diag{[g 1 ,g 2 ,…,g 4M ]} (17)
The block gradient estimate is given by:
wherein p (n) =ifft [ G (n) a H (n)E(n)]The adaptive frequency domain joint multi-branch combining equalization algorithm can be expressed as
Where m represents the step size parameter.
4. The method of claim 2, wherein in order to satisfy the assumption that the channel is unchanged during a packet transmission, a short packet is used, which is composed of a training sequence and data symbols, the number of symbols of the smooth observation training sequence used for weight vector adjustment is limited, and in order to ensure convergence of (19), the training sequence in the packet is repeatedly reused to update the weight vector until the weight vector converges to a steady state, and the weight vector is initialized by the weight value after the last update for each repeated update using the same training sequence.
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