CN103873398B - Joint merges multiple-limb regulatable view window mouthful length equilibrium detection method and device - Google Patents

Abstract

The present invention relates to the cooperative communication technology field in radio communication, it is specifically a kind of in collaboration communication, effectively the reception signal of source node and multiple via nodes can be come from by merging treatment, and significantly improve the joint merging multiple-limb regulatable view window mouthful length equilibrium detection method and device of the self adaptation of systematic function, it is characterised in that to from source node and M（M≥1）The signal of the M+1 branches of individual via node carries out following treatment：The output signal of each branch is first sampled through analog-to-digital conversion, signal after sampling is weighted via corresponding tap coefficient, then it is added as the output of the branch, the output of each branch is added and obtains final output, it is worth noting that the weights of each branch carry out Joint regulation in the training stage using training sequence, and each branch's watch window length can be adjusted to optimal watch window length according to the specific channel envelope per branch.

Description

Combined multi-branch adjustable observation window length balance detection method and device

Technical Field

The invention relates to the technical field of cooperative communication in wireless communication, in particular to a self-adaptive combined multi-branch adjustable observation window length balance detection method and a self-adaptive combined multi-branch adjustable observation window length balance detection device which are used in cooperative communication, can effectively combine and process received signals from a source node and a plurality of relay nodes and obviously improve the system performance.

Background

The cooperative wireless communication technology can realize cooperative diversity without each node comprising a plurality of antennas, and performs cooperative transmission by sharing the antennas of each adjacent node, so as to form a virtual antenna array similar to multi-antenna transmission, so that the cooperative communication combines the advantages of the diversity technology and the relay transmission technology, and the performance gain of multi-antenna and multi-hop transmission can be obtained. Depending on the strategy of relay cooperation, relay networks can be generally divided into decode-and-forward (DF) and amplify-and-forward (AF) networks. Of these two strategies, the AF strategy is more attractive in practical systems because of less computational load on the relay side.

One of the advantages of cooperative communication is that the effective signal-to-noise ratio (SNR) can be improved at the destination node, and thus better system performance can be obtained. For cooperative wireless communication, information sent by a source node reaches a destination node through different transmission paths, wherein one is that the source node directly transmits to the destination node, and the other is that the information reaches the destination node through a relay node. Thus, system performance depends in part on the combining technique of the signals from the source node and the relay node. There are currently a lot of research on combining techniques, such as Maximal Ratio Combining (MRC), Selective Combining (SC) and Switched Diversity Combining (SDC), which all assume that at the destination node, the Channel State Information (CSI) is known among all nodes and the channel is a frequency-flat fading channel. However, in high-speed wireless communication applications, the transmission bandwidth is greater than the associated bandwidth of the channel, such that the channel is frequency selective. For high-speed communication applications in a cooperative communication network, the existing techniques for frequency-flat fading channels need to be improved or new techniques are proposed to eliminate the influence of the frequency-selective channel. A fractional interval 2 tap Fixed Observation Window Length (FOWL) equalization detector has been proposed that combines the input signals from two separate channels, one frequency selective channel (assumed to be from the relay) and the other gaussian channel (assumed to be from the source node). The design of the detector is realized based on Minimum Mean Square Error (MMSE) standard, but the tap coefficient vector of the detector is derived by orthogonal theorem theory on the premise that the CSI among all nodes is known (an adaptive algorithm is not given). In practice, however, at the destination node, the CSI between all nodes is unknown, the channel from the source node may also be a frequency selective channel, and more nodes may participate in cooperative communication. Therefore, it is desirable to consider improving a two-branch FOWL equalization detector so that it can handle these situations. In addition, the wireless channel has environmental dependency (e.g., building geometry) because the environment varies, and the channel envelope varies between different nodes. The Observation Window Length (OWL) is an important parameter for the error rate performance of the equalizer detector, and the optimum OWL required to achieve optimum error rate performance depends on the specific channel envelope, and thus the optimum OWL required is different for different channel envelopes. Especially for an actual cooperative communication system, the channel envelope between the destination node pair and the nodes is unknown, so that the optimal OWL cannot be obtained in advance. In order to achieve better error rate performance, the equalization detector needs to have the capability of adaptively adjusting the OWL of each branch according to the specific channel envelope corresponding to each branch.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a self-adaptive combined multi-branch adjustable observation window length balance detection method and a self-adaptive combined multi-branch adjustable observation window length balance detection device, which are used in cooperative communication, can effectively combine and process received signals from a source node and a plurality of relay nodes and obviously improve the system performance.

The invention can be achieved by the following measures:

a combined multi-branch adjustable observation window length equalization detection method is characterized in that signals of M +1 branches from a source node and M (M is more than or equal to 1) relay nodes are processed as follows: the output signal of each branch is firstly sampled by analog-to-digital conversion, the sampled signals are weighted by corresponding tap coefficients and then added to be used as the output of the branch, the outputs of the branches are added to obtain the final output, and the tap coefficient weights of the branches are jointly adjusted by using a training sequence in a training stage.

In the invention, a received signal from a source node is marked as r_SD(k) The received signal from the relay node is denoted as r_RiD(k) Wherein i is more than or equal to 1 and less than or equal to M, and the received signals of M +1 branches are respectively marked as r after A/D conversion_SD（n），r_R1D（n）……r_RiD（n）M +1 branch signals are respectively sent into M +1 sub-equalizers adopting FIR filtering structures for equalization processing, so that the signals after A/D sampling are weighted by corresponding tap coefficients, wherein the received signals r_SD(k) And r_RiD(k) The signal vector of (d) is noted as:

where m denotes the observation window length of the sub-equalizer, T_fBit symbol duration, T, representing sub-equalizer_sRepresenting the sampling period of the A/D; the samples set within the bit symbol duration form a processing unit, l represents the number of processing units within the observation window;

and u_SD（n） And u_RiD(n)The corresponding tap coefficient vector is defined as follows

Wherein c is_SD(n)And c_RiD(n)Has an initial value of c_i=[0，0，…，0]^TM denotes the observation window length, and further, introduces an (M +1) M-dimensional row vector:

and (M +1) M-dimensional column vector:

（12），

the outputs of M +1 branches are respectively marked as y_SD（n），y_RiD(n), wherein i is more than or equal to 1 and less than or equal to M,

signal y (n) = y finally output after combination_SD（n）+y_R1D（n）+……+y_RMD(n), the tap coefficients of the M +1 sub-equalizers are adaptively adjusted and updated jointly, and the final output is restricted by the following formula:

（13）

in the present invention, the estimation of the transmitted symbol is focused on, but not the estimation of the tap coefficient vector, so the error is defined as

Where d (n) denotes a data symbol transmitted by the source node,denotes the estimation of d (n),

based on the interference minimization principle, the final design objective of the present invention can be expressed as the following constrained optimization problem:

euclidean norm for minimizing tap coefficient vector increment

And subject to the following constraints on the output of a combined-combining multi-branch (JCMB) FOWL equalization detector

WhereinExpressing the euclidean norm, equation (19) is close to (13).

The meaning of the optimum criteria in the present invention is as follows: given an input signal vector u (n), the tap coefficient vector should be changed from c (n) to c (n +1) in a minimum (least mean square sense) manner, so that the output u (n) c (n +1) filtered by updating the tap coefficient c (n +1) will be equal to d (n), and a constraint condition (13) should be satisfied, since the purpose of the proposed detector is to jointly combine and process signals from a source node and a plurality of relay nodes to obtain an estimate of the transmitted signal, and in order to solve this constraint optimization problem, the cost function of the proposed detector is set as:

wherein,in order to be a lagrange multiplier,the square operation of expressing the Euclidean norm, the cost function (20) is led to c (n +1) to be derived, and the derivation result is zero, so that the following can be obtained:

substituting (21) into formula (19) to obtain an unknown multiplierIs provided with

To overcome the problem of gradient noise amplification, parameters are introducedCombining the results of equations (21) and (22), one can derive a linear estimate based on the MMSE criterion:

the self-adaptive joint merging processing algorithm comprises the following steps:

where the sum of μ is a positive constant, unlike single-tap equalization detectors for point-to-point communications, the tap coefficient vector (c) of the present invention_SD（n）And) And the combining process is jointly selected based on MMSE criteria.

The invention also comprises the adjustment of the length of the M +1 multi-path branch adjustable observation window, which specifically comprises the following steps: let the steady state observation window length of branch i be L_i，Andrepresenting the corresponding steady state tap coefficient vector and input signal vector, respectively, and n represents the discrete time index, the branch segment steady state error SSE is defined as:

where d (n) represents the desired signal,，andare respectively a tap coefficient vectorAnd input signal vectorIs defined as the mean square SSE of

In order to complete the adjustment of the length of the observation window, firstly, a cost function for OWL adjustment is set, and the cost function for searching the optimal OWL in each branch is defined as

WhereinAndthe normal number is preset according to system requirements.

Andrespectively represent a corresponding S-D and R_iOWL of the-D branch is L_SDAnd L_RiDThe steady state mean square SSE of the time,andrespectively represent S-D and R_iSteady state mean square SSE of the D branch:

wherein

The different outputs of the various branches may be used to calculate respective error signals:

in order to solve the problem, a pseudo-fractional OWL (PF-OWL) concept is adopted to enable instantaneous adjustment of OWL, the observation window length is not limited to an integer, the real OWL is not changed until PF-OWL is accumulated to a certain degree, and based on cost functions (26) and (27), JCMB-AOWL equalization based on MMSE standard can be obtained, and the algorithm is as follows:

whereinThe pseudo-score OWL of the corresponding branch is represented, parameters α and gamma are normal numbers, α is a leakage factor, and α < gamma, L_k（n）Representing the corresponding branchThe true OWL at a discrete time instant n,has an initial value ofWhen is coming into contact withExceeds a certain threshold value, L_k（n）Rounding update is performed as follows:

whereinExpress andthe most recent integer, η, is a small integer threshold.

According to equations (36) and (37), the true OWL L_k（n）Is not changed untilAccumulated to a certain degreeDefinition assuming that OWL of all branches changes

Andrepresents the signal vector after the OWL adjustment,andand expressing the corresponding tap coefficient vector after adjustment, wherein the adaptive adjustment process of the tap coefficient is as follows:

firstly, with R_iThe D branch exemplifies the change of the signal vector and the tap coefficient vector,

if p is increased_aAnd each tap:

if p is decreased_rAnd each tap:

wherein p is_aAnd p_rIs determined by equation (37).

After adjustment by the OWL branches, the signal vector and the tap coefficient vector of the proposed equalization detector can be expressed as

WhereinAndrespectively representing the signal vector and the tap coefficient vector via the adjusted respective branch, and then, the tap coefficients: (And) And channel combination is processed based on MMSE standard, and the updating algorithm is as follows:

in the invention, the tap coefficients of all branches and the merging processing are jointly realized, so the errors calculated by the formulas (30) to (33) are all subsection steady-state errorsAnd (4) poor. This is because the tap coefficient vector represented by the formula (43) consists ofAndis composed ofAndeach is a segment tap coefficient vector of the tap coefficient vector shown in equation (43).

The invention also provides a balance detection device adopting the method for detecting the length of the combined and combined multi-branch adjustable observation window, which is characterized by correspondingly comprising 1 source node S, 1 destination node D and more than two relay nodes R_iI =1,2,3, … …, where the source node, the destination node, and the relay node all only include 1 transmitting antenna and 1 receiving antenna and are in a half-duplex cooperative communication system, there are M +1 signals r respectively used for processing the signals received by the destination node from the source node_SD（k）And M paths of signals r received by the source node from the relay node_RiD（k）The sub-equalizer is also provided with a self-adaptive adjuster used for self-adaptively adjusting tap coefficients of the M +1 sub-equalizers, the sub-equalizers adopt an FIR filter structure, and the outputs of the M +1 sub-equalizers are added by the superimposer to obtain a combined signal.

The invention also comprises M +1 length regulators which are respectively connected with the M +1 sub-equalizers in a one-to-one correspondence manner and used for adjusting the length of the observation window, wherein the length adjusting period comprises a subsection steady-state error calculating unit, a PF-OWL adjusting unit connected with the output end of the subsection steady-state error calculating unit and an OWL adjusting unit connected with the output end of the PF-OWL adjusting unit, and the output end of the OWL adjusting unit is correspondingly connected with the sub-equalizers in the path.

Compared with the prior art, the invention can jointly process signals (source node-destination node and a plurality of source nodes-relay nodes-destination node) from a plurality of independent frequency selective channels, the tap coefficients of all branches are jointly adjusted by using the adaptive algorithm aiming at the specific channel corresponding to each branch, and at the destination node end, CSI among all nodes is not required to be assumed to be known.

Description of the drawings:

fig. 1 is a system model diagram of a single-source node and single-destination node multi-relay cooperative network.

FIG. 2 is a schematic diagram of the present invention.

Fig. 3 is another schematic structure of the present invention.

Fig. 4 is a one-time channel implementation based on equation (1) in the present invention.

Fig. 5 is a graph of the BER performance (SNR =10dB) of the equalization detector under different OWLs in the present invention.

FIG. 6 is the OWL evolution (SNR =10dB) of the AOWL equalization detector of the present invention.

FIG. 7 is a comparison of the bit error rate performance of the combined merge 2-branch FOWL equalization detector of the present invention and the prior art.

FIG. 8 shows the BER performance of the JCMB-FOWL equalization detection device of the present invention when combining (SC) MB-FOWL and JCMB-FOWL equalization detection devices, respectively, using different numbers of cooperating nodes.

FIG. 9 is a graph comparing the BER performance of SCMB-AOWL, JCMB-FOWL and the JCMB-AOWL equalization detector of the present invention using different numbers of cooperative relays (M =1 and M = 2).

The specific implementation mode is as follows:

the invention will be further described with reference to the accompanying drawings in which:

as shown in fig. 1, the following system is taken as an example, the present invention provides a method and an apparatus for detecting length equalization of a combined and merged multi-branch adjustable observation window, and the system comprises 1 source node (S), 1 destination node (D) and a plurality of relay nodes (S) (r)) And (4) forming. The source node, the destination node and the relay node only comprise 1 transmitting antenna and 1 receiving antenna, are in a half-duplex mode and cannot simultaneously receive and transmit signals. It is assumed that the relays are operating in an Amplified Forwarding (AF) mode and that all relays have the same average power limit. The one-time transmission process comprises two stages: in the first stage, a source node broadcasts information to a destination node and a relay node; in the second stage, the relay amplifies and forwards the noisy signal received in the first stage to the destination node.

S→D,S→R_iAnd R_i→D(For the ith relay) are respectively recorded as，

Andwherein L is_SD，L_SRAnd L_RiDRespectively, representing the corresponding channel memory length. The channel between all nodes is assumed to be frequency selective channel, and the discrete time channel impulse response is modeled by zero mean value Gaussian random variable obeying exponential power delay envelope

Wherein，The delay spread characteristics of the channel are described,representing the Dirac function, P_RRepresenting the average power of the multipath component.

In the first phase, the signals received by the ith relay and destination node are given by

Where s (k) represents the signal sent by the source node,andrepresenting additive white Gaussian noise, the mean value is zero, and the variance is respectivelyAnd。

in the second stage, the destination node relays R from the ith relay_iThe received signal is

Wherein n is_RiD（k）Representing the corresponding additive white Gaussian noise, with a mean of zero and a variance of。

β_iIs a relayIn order to meet its power limit, relays are setHas a gain of

Where P is the average energy per symbol transmitted by each node,is a source node and a relayK-th channel coefficient of inter-channel, N_0，iRepresenting the variance of zero mean gaussian white noise in the signal received at the destination node from the ith relay.

Example 1:

as shown in fig. 2, the present invention firstly provides a method and an apparatus for detecting length equalization of a combined and merged multi-branch fixed observation window, and in particular, a device for detecting length equalization of a combined and merged multi-branch fixed observation window (JCMB-FOWL equalization device), which employs a fractionally spaced linear filter structure, and can jointly implement functions of matched filtering and symbol interval equalization, in order to effectively combine and process signals transmitted through a plurality of independent frequency selective channels, embodiment 1 provides a combined and merged (JC) multi-branch (MB) Fixed Observation Window Length (FOWL) equalization detector, whose structure is shown in fig. 2, and the structure is different from that of a conventional equalization detector for point-to-point communication, because the detector has to jointly process received signals corresponding to a plurality of channels. The proposed equalization detector will jointly implement matched filtering, combining processing and adaptive equalization;

the equalization detector will directly process the sampled signal after analog-to-digital conversion (A/D), for S-D and S-R_i-D receiving a signal, setting the input signal of the equalization detector asAnd. In each bit period, the output of the equalizing detector produces a decision, the tap coefficients of each branch are jointly adjusted by the adaptive filtering algorithm, the adaptive operation is carried out in the training stage, the training sequence is used for adapting to the channel, and then in the direct decision mode, the hard decision is used as the output of the equalizing detector.

In a JCMB-FOWL equalization detector, corresponding to a received signalAndthe signal vector of (a) is given by:

where m denotes the observation window length, T_fRepresenting bit symbol duration, T_sThe sampling period of the a/D is indicated. The samples set within the bit symbol duration form a processing unit, l represents the number of processing units within the observation window.

Correspond toAndthe tap coefficient vector of (a) is defined as follows:

whereinAndis initially ofM represents a viewing window length, defined in formula (8);

introducing an (M +1) M-dimensional row vector:

and (M +1) M-dimensional column vector

In the proposed detector, the outputs of multiple channels are jointly processed to estimate the transmitted data symbols. The output of the JCMB-FOWL equalization detector is therefore constrained by the following equation:

the detector focuses on the estimation of the transmitted symbols, rather than on the tap coefficient vector, defining the error as:

where d (n) denotes a data symbol transmitted by the source node,represents the estimation of d (n):

wherein

Based on the interference minimization principle, the design criteria of the JCMB-FOWL equalization detector can be expressed as the following constrained optimization problem.

Euclidean norm for minimizing tap coefficient vector increment

And subject to the following constraints on the output of the JCMB-FOWL equalization detector

WhereinExpressing the euclidean norm, equation (19) is close to (13).

The meaning of the optimum criterion is as follows: given an input signal vector u (n), the tap coefficient vector should be changed from c (n) to c (n +1) in a minimal (least mean square sense) manner such that the output filtered by the updated tap coefficient c (n +1)Will be equal to d (n). The constraint (13) should be satisfied, since the purpose of the proposed detector is to combine the processing from the source node and the plurality of relay nodesThe signal of the point, an estimate of the transmitted signal is obtained.

To solve this constraint optimization problem, the lagrange multiplier method is used, and the cost function of the proposed detector is represented by equations (18) and (19)

Wherein ξ is Lagrange multiplier,representing the square operation of the euclidean norm. The cost function (20) is used for differentiating c (n +1), and the differentiation result is zero, so that the method can be obtained

To solve for the unknown multiplier xi, formula (19) is substituted with (21), having

To overcome the problem of gradient noise amplification, parameters are introduced. Combining the results of equations (21) and (22) gives linear estimates based on the MMSE criterionThe adaptive algorithm of (2):

where the sum of μ is a normal number. Unlike a single-branch equalization detector for point-to-point communication, in a JCMB-FOWL equalization detector, a tap coefficient vector (c)_SD（n）And c_RiD（n）) And the combining process is jointly selected based on MMSE criteria. In practice, as shown in fig. 2, the output of each branch is first weighted by the corresponding tap coefficient and then added as the output of that branch, the output of the proposed detector being the addition of the outputs of the branches. The weights of the branches are jointly adjusted in the training phase using a training sequence. The weight value determines the importance of each branch on the detection performance.

Example 2:

the invention also provides a JCMB-AOWL equalization detection device based on MMSE standard, the structure of the detection device is shown in figure 3:

wherein the segmentation Steady State Error (SSE) and the mean square SSE are defined as follows: let the steady state observation window length of branch i be L_i，Andrepresenting the corresponding steady-state tap coefficient vector and input signal vector, respectively, and n represents the discrete-time index, the branch SSE is defined as:

where d (n) represents the desired signal,，andare respectively a tap coefficient vectorAnd input signal vectorThe mean square SSE of can be defined as

In order to design and realize a JCMB-AOWL equilibrium detector, firstly, a cost function for OWL adjustment is set, and the cost function for searching the optimal OWL in each branch is defined as

WhereinAndthe normal number is preset according to system requirements.Andrespectively represent a corresponding S-D and R_iOWL of the-D branch isAndsteady state mean square SSE of time.Andrespectively represent S-D and R_iSteady state mean square SSE of the D branch, respectively:

wherein

Different outputs of each branch of the equalization detector may be used to calculate corresponding error signals

The corresponding OWL can be solved using the gradient method based on the criteria (26) and (27), however, OWL must be an integer, which constrains the adaptive adjustment of OWL. To solve this problem, a pseudo-fractional OWL (PF-OWL) concept is employed to enable instantaneous adjustment of OWL, where the observation window length is no longer limited to integers, and the true OWL does not change until PF-OWL accumulates to some extent. Based on cost functions (26) and (27), JCMB-AOWL equalization based on MMSE criterion can be obtained as follows

WhereinThe pseudo-score OWL parameters α and gamma of the corresponding branch are normal numbers, gamma is a leakage factor, and α < gamma.L needs to be satisfied_k（n）Representing the corresponding branchThe true OWL at discrete time n.Has an initial value of. When in useExceeds a certain threshold value, L_k（n）Rounding update is performed as follows:

whereinExpress andthe most recent integer number is the number of integers,is a small integer threshold.

True OWLL according to equations (36) and (37)_k（n）Is not changed untilAccumulated to a certain degree. Assuming that OWL of all branches changes, defineAndrepresents the signal vector after the OWL adjustment,andrepresenting the adjusted corresponding tap coefficient vector. The adaptive adjustment process of the tap coefficients is as follows.

First, withThe branch is an example of the change of the signal vector and the tap coefficient vector.

If p is increased_aAnd each tap:

if p is decreased_rAnd each tap:

wherein p is_aAnd p_rIs determined by equation (37).

After adjustment by the OWL branches, the signal vector and the tap coefficient vector of the proposed equalization detector can be expressed as

WhereinAndrespectively representing the signal vector and the tap coefficient vector via the adjusted respective branch.

Then, tap coefficients (And) The sum channel is merged and is processed based on MMSE standard, and the updating algorithm is as follows

In the proposed equalization detector, the tap coefficients of all branches and the combining process are jointly implemented, so it is worth noting that the errors calculated by equations (30) - (33) are all piecewise steady-state errors of the detector. This is because the tap coefficient vector represented by the formula (43) consists ofAndis composed ofAndeach is a segment tap coefficient vector of the tap coefficient vector shown in equation (43).

The performance of the invention is further analyzed by combining the simulation result as follows:

in order to evaluate the performance of the JCMB equalization detection device provided by the present invention, a modulation mode adopts binary phase modulation (BPSK), all nodes are assumed to work at the same power, in simulation, a cooperative communication network with a single transmitting-receiving pair and a plurality of relays is considered, and channels among the nodes are all semi-stable frequency selective channels. The channel impulse response coefficient is modeled by formula (1) and the parameters are set，，，，，. The parameter μ sum in each of the formulas (23) and (46) is set to 0.5. For the FOWL equalization detector, OWL is set to 3-bit symbol duration, i.e., in equations (6) and (7). For the AOWL equalization detector, the initial OWL is also set toLeakage factor α is 0.005 and parameter η is set to 1.

(1) Self-adaptive adjustment and verification of the length of the observation window:

the adjustment capability of the proposed JCMB-AOWL equalization detector on OWL is first verified. To facilitate verification of adaptive OWL adjustment, point-to-point communication cases (S-D) are considered, with no relay participating in cooperative transmission. The simulations are for the same channel. Figure 4 shows the impulse response of the channel generated by the channel model (1).

For the channel shown in fig. 4, the optimal OWL is difficult to obtain directly due to the non-linear relationship between OWL and steady state MSE. To verify that the AOWL equalization detector can adaptively adjust OWL according to the specific channel envelope. First, the optimal OWL required for the channel envelope in fig. 4 will be obtained in a simulation method. Figure 5 shows the BER performance of the equalization detector when using different OWLs. For this channel, 100 data packets are transmitted, each packet containing 10000 symbols, 500 of which are used as training sequences. Each point on the BER curve is derived from the error rate for an average of 100 packets. It can be seen from the graph that the BER performance is very close when OWL is 45-105. However, increasing OWL will increase the complexity of the detector, and considering the complexity problem, the optimal OWL is defined as the minimum OWL that enables the equalized detector to achieve near optimal BER performance. By this definition, the optimal OWL is approximately 45 as seen in fig. 5.

Next, the ability of the AOWL equalization detector to adjust OWL was evaluated. In simulation, parametersSet to 5. For the same channel, 10 data packets are transmitted, each containing 15000 symbols. The OWL evolution curve is obtained by averaging the OWL evolution curves under each data packet. FIG. 6 shows the OWL evolution of the AOWL equilibrium detector. For the specific channel envelope shown in fig. 4, it can be seen from fig. 6 that the proposed equalization detector can adaptively adjust OWL to around 45. The resulting OWL approximates the optimal OWL in fig. 5.

From the above simulation results, it can be seen that the ability of the AOWL equalization detector to adaptively adjust OWL according to the specific channel envelope is verified.

(2) And (3) bit error rate performance comparison:

in order to compare the error rate performance of different equalization detection schemes, a monte carlo simulation is established based on the channel model (1). In the simulation, the received signal-to-noise ratios (S-D and R) of the received signals from the source node and the relay nodes are the same at the destination node end_iParameters of the D branchSet to 6 and 8, respectively. For each channel realization, a data packet is sent with 10000 symbols, 500 symbols of which are used as training sequences. The BER performance and the OWL evolution curve are respectively obtained by averaging the error rate and the OWL evolution curve under 500 random channel realization.

First, the proposed JCMB-FOWL equalization detector and literature [ S. Wei, D.L. Goeckel, and M.C. Valenti, "Ashchronous cooperative diversity," IEEE Trans "were comparedactions onWireless Communications, vol. 5, no. 6, pp. 1547-1557, Jun. 2006.]BER performance of the scheme in (1), documents [ S. Wei, D.L. Goeckel, and M.C. Valenti, "Asychronous collaborative," IEEE Transactions on Wireless Communications, vol.5, No. 6, pp.1547-1557, Jun.2006.]The scheme in (1) is to deduce a tap coefficient vector of the detector based on MMSE standard and applying the principle of orthogonality on the premise that the CSI between the nodes is known (only 2 branches are considered, one branch corresponds to a frequency selective channel, and the other branch corresponds to a gaussian channel). By comparing the performance, the correctness of the proposed adaptive equalization detector can be verified. FIG. 7 shows a 2-branch FOWL equalization detector and document [21 ]]BER performance of the scheme (iii). For the documents S.Wei, D.L.Goeckel, and M.C. Valenti, "Ashchronus cooperative diversity," IEEETransactions on Wireless Communications, vol.5, No. 6, pp. 1547-.]The solution (a) to (b) in (b),wherein，Correlation matrix in simulationObtained by a time averaging methodFor estimatingThe number of symbols N of (2) is 2000. As can be seen from the figure, the BER performance of the JCMB-FOWL equalization detector is close to that of the documents S.Wei, D.L. Goeckel, and M.C. Valenti, "Ashchronus cooperative diversity," IEEE Transactions on Wireless communications, vol.5, No. 6, pp.1547, Jun.2006.]The scheme in (1). The simulation result verifies the correctness of the proposed scheme. Practice ofIn the practical cooperative communication system, the CSI between the nodes is unknown at the destination node end, and the JCMB-FOWL can adaptively adjust the tap coefficient according to the specific channel, so that the proposed scheme is more suitable for the practical cooperative communication system.

Next, the impact of the number of cooperative nodes on the BER performance of the JCMB-FOWL equalization detector is evaluated. FIG. 8 shows the BER performance of the combined (SC) MB-FOWL and proposed JCMB-FOWL equalization detectors, respectively, when different numbers of cooperating nodes are used. The SC scheme refers to a combining method in which tap coefficients of each branch in the MB equalization detector are independently obtained similarly to the case of point-to-point communication, and then the outputs of the branches are added with equal gains to serve as the outputs of the detector. JC and SC schemes in the simulation use the same simulation parameters. It can be seen from the figure that the BER performance of the JCMB-FOWL equalization detector improves significantly as the number of cooperative nodes increases (when BER = 10)^-5When compared to using 1 relay, approximately 8dB performance gain can be achieved with 3 relays cooperation and approximately 12dB performance gain with 5 relays). This is because the number of cooperative nodes is an important factor affecting BER performance, the diversity gain of single relay cooperation is limited, and the larger the number of cooperative nodes is, the larger the obtained diversity gain is. In addition, it can be seen from the figure that the BER performance achieved by the proposed JC scheme is better than that of the SC scheme. In particular, when BER =10^-3With 1, 3 and 5 cooperative relays, the JCMB-FOWL can achieve 4dB, 4dB and 3.8dB performance gains respectively over the SCMB-FOWL equalization detector. This is because the tap coefficients of all branches in the JC scheme are jointly obtained based on the MMSE criterion, the weight of each branch is jointly adjusted using the training sequence in the training phase, the size of the weight determines the importance of each branch on the detection performance, and the SC scheme only employs equal gain combining.

Finally, the performance of the proposed JCMB-AOWL equalization detector was investigated and compared with the performance of the JCMB-FOWL equalization detector. FIG. 9 illustrates that the performance of the JCMB-AOWL equalization detector outperforms the SCMB-AOWL and JCMB-FOWL equalization detectors. The same simulation parameters were used for the 3 detectors in the simulation, and the step size parameter γ was set to 3 for the JC and SC MB-AOWL detectors. SC scheme refers to OWL and tap of each branch in MB equalization detectorThe coefficients are obtained independently similar to the point-to-point communication case, and then the equal gain summation of the outputs of the branches is used as the merging method of the detector outputs. As can be seen from the graph, the BER performance achieved by the JCMB-AOWL equalization detector is better than that achieved by the SCMB-AOWL (when BER = 10)^-5In this case, approximately 2.8dB performance gain is obtained with 1 relay, and approximately 2.3dB performance gain is obtained with 2 relays). This is because in the JCMB-AOWL equalization detector, the tap coefficients of all branches are jointly obtained. Furthermore, it is noteworthy that JCMB-AOWL achieved significant performance gains over JCMB-FOWL. In particular, when BER =10^-5With 1 and 2 cooperative relays, the JCMB-AOWL can achieve approximately 5dB and 4dB performance gains, respectively, over the JCMB-FOWL equalization detector. This is because OWL is an important parameter for BER performance, and the JCMB-AOWL equalization detector can adaptively adjust the OWL of each branch according to the specific channel envelope corresponding to each branch.

The invention provides a joint combination multi-branch (JCMB) equalization detector scheme aiming at a cooperative communication network under a frequency selective channel. To evaluate the performance of the proposed scheme, a monte carlo simulation was built based on the frequency selective channel model (1). Simulation results show that the JCMB-AOWL equalization detector can adaptively adjust the length of an observation window according to specific channel envelopes. Compared with the scheme deduced by the existing theory of assuming the known CSI among nodes, the JCMB-FOWL equalization detector does not need to assume that the CSI is known and can obtain very close BER performance. Under the same simulation parameter setting, the performance obtained by the JC is superior to that of the SC equilibrium detector. Simulation results also show that JCMB-AOWL can achieve better BER performance than the JCMB-FOWL equalization detector.

Claims (4)

1. A combined multi-branch adjustable observation window length equalization detection method is characterized in that signals of M +1 branches from a source node and M (M is more than or equal to 1) relay nodes are processed as follows: the output signal of each branch is sampled by analog-to-digital conversion, the sampled signals are weighted by corresponding tap coefficients and then added to be used as the output of the branch, the outputs of the branches are added to obtain the final output, and the weight of each branch is jointly adjusted by using a training sequence in a training stage;

the received signal from the source node is denoted as r_SD(k) The received signal from the relay node is denoted asWherein i is more than or equal to 1 and less than or equal to M, and the received signals of M +1 branch are respectively marked asThe M +1 branch signals are respectively sent to M +1 sub-equalizers adopting FIR filtering structures for equalization processing, so that the signals after A/D sampling are weighted by corresponding tap coefficients, wherein the received signals r_SD(k) Andthe signal vector of (d) is noted as:

u_SD(n)＝[r_SD(n-1)，r_SD(n-2)，…，r_SD(n-m)]

$u_{R_{i}D}\left(n\right)=\&lsqb;r_{R_{i}D}(n-1),r_{R_{i}D}(n-2),\mathrm{...},r_{R_{i}D}(n-m)\&rsqb;$

m＝lT_f/T_swhere m denotes the observation window length of the sub-equalizer, T_fBit symbol duration, T, representing sub-equalizer_sRepresenting the sampling period of the A/D; the samples set within the bit symbol duration form a processing unit, l represents the number of processing units within the observation window;

and u_SD(n) andthe corresponding tap coefficient vector is defined as follows

c_SD(n)＝[c_SD，n1，c_SD，n2，…，c_SD，nm]^T

$c_{R_{i}D}\left(n\right)={\&lsqb;c_{R_{i}D,n1},c_{R_{i}D,n2},\mathrm{...},c_{R_{i}D,nm}\&rsqb;}^T$

Wherein c is_SD(n) andhas an initial value of c_i＝[0，0，…，0]^TM denotes the observation window length, and further, introduces an (M +1) M-dimensional row vector:

$u\left(n\right)=\&lsqb;u_{SD}\left(n\right),u_{R_{1}D}\left(n\right),u_{R_{2}D}\left(n\right),...,u_{R_{M}D}\left(n\right)\&rsqb;$

and (M +1) M-dimensional column vector:

the outputs of M +1 branches are respectively marked as y_SD(n)，Wherein i is more than or equal to 1 and less than or equal to M, y_SD(n)＝u_SD(n)c_SD(n)

$y_{R_{i}D}\left(n\right)=u_{R_{i}D}\left(n\right)c_{R_{i}D}\left(n\right)$

Combined and finally output signalIn the process, the adaptive regulator respectively updates tap coefficients for the outputs of the M +1 paths, so that the final output is restricted by the following formula:

$d\left(n\right)=u\left(n\right)c\left(n\right)=u_{SD}\left(n\right)c_{SD}\left(n\right)+\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{u}_{R_{i}D}\left(n\right)c_{R_{i}D}\left(n\right).$

2. a method as claimed in claim 1, wherein the estimation of the transmitted symbols is considered instead of the estimation of the tap coefficient vector, so that the error defined during the adaptive adjustment process is:

$e\left(n\right)=d\left(n\right)-\hat{d}\left(n\right)$

where d (n) denotes a data symbol transmitted by the source node,denotes the estimation of d (n),

based on the interference minimization principle, minimizing the Euclidean norm of the tap coefficient vector increment:

||Δc(n)||＝||c(n+1)-c(n)||²

and subject to the following constraints:

$d\left(n\right)=u\left(n\right)c(n+1)=u_{SD}\left(n\right)c_{SD}(n+1)+\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{u}_{R_{i}D}\left(n\right)c_{R_{i}D}(n+1)$

where | · | | represents the euclidean norm.

3. The method for detecting length equalization of a combined and merged multi-branch adjustable observation window according to claim 2, further comprising adjusting the length of the M +1 multi-branch adjustable observation window, specifically: let the steady state observation window length of branch i be L_i，Andrepresenting the corresponding steady state tap coefficient vector and input signal vector, respectively, and n represents the discrete time index, the branch segment steady state error SSE is defined as:

$e_M^{L_i}\left(n\right)=d\left(n\right)-c_{L_i,1:M}^T\left(n\right)u_{L_i,1:M}\left(n\right)$

where d (n) represents the desired signal, 1. ltoreq. M. ltoreq.L_i，Andare respectively a tap coefficient vectorAnd input signal vectorIs defined as the mean square SSE of

${\mathrm{\&xi;}}_M^{L_i}=E\left\{{\left(e_M^{L_i}\right(n\left)\right)}^2\right\}$

In order to complete the adjustment of the length of the observation window, a cost function for adjusting the length OWL of the observation window is firstly set, and the cost function for searching the optimal length OWL of the observation window in each branch is defined as

${\mathrm{\&xi;}}_{L_{SD}-\mathrm{\&Delta;}}^{L_{SD}}-{\mathrm{\&xi;}}_{L_{SD}}^{L_{SD}}\&le;{\mathrm{\&epsiv;}}_{SD}$

${\mathrm{\&xi;}}_{L_{R_{i}D}-\mathrm{\&Delta;}}^{L_{R_{i}D}}-{\mathrm{\&xi;}}_{L_{R_{i}D}}^{L_{R_{i}D}}\&le;{\mathrm{\&epsiv;}}_{R_{i}D}$

Wherein_SDAndaccording to the preset requirements of the system,andrespectively represent a corresponding S-D and R_iThe observation window length OWL of the D branch is L_SDAnd L_RiDThe steady state mean square SSE of the time,andrespectively represent S-D and R_iSteady state mean square SSE of the D branch:

${\mathrm{\&xi;}}_{L_{SD}-\mathrm{\&Delta;}}^{L_{SD}}=E\left\{{\left(e_{L_{SD}-\mathrm{\&Delta;}}^{L_{SD}}\right(n\left)\right)}^2\right\}$

${\mathrm{\&xi;}}_{L_{R_{i}D}-\mathrm{\&Delta;}}^{L_{R_{i}D}}=E\left\{{\left(e_{L_{R_{i}D}-\mathrm{\&Delta;}}^{L_{R_{i}D}}\right(n\left)\right)}^2\right\}$

wherein

$e_{L_{SD}-\mathrm{\&Delta;}}^{L_{SD}}\left(n\right)=d\left(n\right)-c_{L_{SD},1:L_{SD}-\mathrm{\&Delta;}}^T\left(n\right)u_{L_{SD},1:L_{SD}-\mathrm{\&Delta;}}\left(n\right)$

$e_{L_{R_{i}D}-\mathrm{\&Delta;}}^{L_{R_{i}D}}\left(n\right)=d\left(n\right)-c_{L_{R_{i}D},1:L_{R_{i}D}-\mathrm{\&Delta;}}^T\left(n\right)u_{L_{R_{i}D},1:L_{R_{i}D}-\mathrm{\&Delta;}}\left(n\right)$

The different outputs of the various branches may be used to calculate respective error signals:

$e_{L_{SD}}^{L_{SD}}\left(n\right)=d\left(n\right)-y_{SD}\left(n\right)=d\left(n\right)-c_{L_{SD}}\left(n\right)u_{L_{SD}}\left(n\right)$

$e_{L_{R_{i}D}}^{L_{R_{i}D}}\left(n\right)=d\left(n\right)-y_{R_{i}D}\left(n\right)=d\left(n\right)-c_{L_{R_{i}D}}\left(n\right)u_{L_{R_{i}D}}\left(n\right),i=1,...,M$

based on a standard by means of a gradient methodAndthe corresponding observation window length OWL is obtained, but the observation window length OWL must be an integer, which constrains the observation windowIn order to solve the problem, a pseudo-fractional OWL (PF-OWL) concept is adopted to instantaneously adjust the observation window length OWL, and then the observation window length is not limited to an integer, and the real observation window length OWL is not changed until the PF-OWL is accumulated to a certain extent, so that JCMB-AOWL equalization based on MMSE standard can be obtained, and the algorithm is as follows:

$l_{f,SD}(n+1)=\left(l_{f,SD}\right(n)-\mathrm{\&alpha;})-\mathrm{\&gamma;}\&lsqb;{\left(e_{L_{SD}\left(n\right)}^{L_{SD}\left(n\right)}\right)}^2-{\left(e_{L_{SD}\left(n\right)-\mathrm{\&Delta;}}^{L_{SD}\left(n\right)}\right)}^2\&rsqb;$

$l_{f,R_{i}D}(n+1)=\left(l_{f,R_{i}D}\right(n)-\mathrm{\&alpha;})-\mathrm{\&gamma;}\&lsqb;{\left(e_{L_{R_{i}D}\left(n\right)}^{L_{R_{i}D}\left(n\right)}\right)}^2-{\left(e_{L_{R_{i}D}\left(n\right)-\mathrm{\&Delta;}}^{L_{R_{i}D}\left(n\right)}\right)}^2\&rsqb;$

wherein l_f，k(n) the pseudo-fraction OWL of the corresponding branch, the parameters α and gamma are normal numbers, α is a leakage factor, and the requirements of α < gamma and L are met_k(n)Represents the corresponding branch l_f，k(n) true OWL, l at discrete time n_f，kThe initial value of (n) is l_f，k(0)＝L_k(0) When l is_f，k(n) ofCumulative amount of change exceeds a certain threshold, L_k(n)Rounding update is performed as follows:

whereinRepresents taking and l_f，k(n) the nearest integer, η is an integer threshold.

4. An equalization detection apparatus using the method of equalizing detection of jointly merging multiple-branch adjustable observation window lengths according to any one of claims 1 to 3, characterized by comprising 1 source node S, 1 destination node D and more than two relay nodes R_iIn a cooperative communication system in which i is 1,2,3, … …, and the source node, the destination node, and the relay node all only include 1 transmitting antenna and 1 receiving antenna and are in a half-duplex mode, there are M +1 signals r respectively used for processing the signal received by the destination node from the source node_SD(k)And M paths of signals r received by the source node from the relay node_RiD(k)The sub-equalizer is also provided with a self-adaptive adjuster used for self-adaptively adjusting tap coefficients of the M +1 sub-equalizers, the sub-equalizers adopt an FIR filter structure, and the outputs of the M +1 sub-equalizers are added by the superimposer to obtain a combined signal.