CN103618585A - A joint multi-user detecting and decoding method based on a belief propagation algorithm - Google Patents

Abstract

The invention relates to a joint multi-user detecting and decoding method based on a belief propagation algorithm and belongs to communication technical field, and in particular, relates to a joint multi-user detecting and decoding method for eliminating interference among multiple users based on a belief propagation algorithm in a multi-user communication system. The extrinsic information of bits and the extrinsic information of symbols of a user and are repeatedly iterated at encoding/decoding nodes and mapping/inverse mapping nodes. After the number of the iteration is satisfied, the joint multi-user detecting and decoding method may achieve joint interference elimination and decoding among multiple users. Compared with a conventional multi-user interference eliminating method, the joint multi-user detecting and decoding method is capable of effectively decreasing the computation complexity of an interference eliminating algorithm and increasing the performance of the interference elimination.

Description

A kind of associating Multiuser Detection and interpretation method based on belief propagation algorithm

Technical field

Associating Multiuser Detection and interpretation method based on belief propagation algorithm belong to a communication technical field, associating Multiuser Detection and the interpretation method particularly between a kind of elimination multi-user based on belief propagation algorithm in multi-user comm, disturbed.

Background technology

In order to reduce costs and utilize efficiently the resource of communication system, existing communication system allows a plurality of common-user system resources mostly.Generally can adopt time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), the different modes such as code division multiple access (Code Division Multiple Access, CDMA) are distinguished different users.Theoretic these multi-access modes can fully be distinguished each user, between different user, can not produce interference, but ambient noise or synchronous error will cause existing and disturbing between user in practical communication system, inter-user interference can reduce the performance of system.

Conventional method is regarded inter-user interference as additive noise in environment, detects independently each user's information.Along with the increase of number of users, inter-user interference can increase, and the performance of whole communication system can decline.Compare with conventional method, multiuser detection is no longer used as other users' interference as noise and is processed, but combine all users that consider busy channel, and weaken or eliminate the interference of other user to arbitrary user, detect certain user or all these users' information simultaneously.Traditional multi-user test method is divided into Linear Multiuser Detection method and Multi-User Detection method.Multiuser Detection linear algorithm adopts linear operator L docking to collect mail to cease and processes.The basic thought of Multi-User Detection algorithm is detected subscriber signal of reconstruct, after it is eliminated from receive signal, detects other users again.These two kinds of methods all can relate to matrix inversion operation in processing procedure, and computation complexity is higher; In addition, traditional receiving handling method separates independent process by detection and decoding, first detects decoding again, cannot realize combined optimization.

The present invention proposes a kind of associating Multiuser Detection and interpretation method based on belief propagation algorithm.The present invention adopts belief propagation algorithm, utilize the structure of factor graph that the external information between the external information between symbol and intersymbol external information, symbol and bit and bit and bit is carried out to iteration and renewal repeatedly, to obtain maximum a posteriori probability, combine interference elimination and decoding.Under linear computation complexity, the present invention can realize good interference elimination performance.

Summary of the invention

The object of this invention is to provide a kind of associating Multiuser Detection and interpretation method based on belief propagation algorithm.The present invention uses belief propagation algorithm, by the external information of the external information of user's bit and symbol at " coding/decoding node " with " mapping/anti-mapping node " iterates, after meeting iterations, the present invention can realize disturbing combining between multi-user and eliminate and decoding.Compare with traditional multi-user interference eliminating method, the present invention can effectively reduce the computation complexity of interference cancellation algorithm and improve and disturb the performance of eliminating.

A kind of associating Multiuser Detection and interpretation method based on belief propagation algorithm be characterised in that, described method detailed process is according to following steps, to realize successively:

Step (1), initialization:

I the symbol sending of first setting in iteration detection method is x _i, x _iaverage and variance the t time iteration are used

with

represent, iterative algorithm initial phase t=1, arranges,

${\mathrm{\ζ}}_{x_i}^{t-1}=0,{\mathrm{\γ}}_{x_i}^{t-1}=1,$

Meanwhile, owing to sending, adopt QPSK modulation, a transmission symbol correspondence 2 coded-bits, in the t time iteration, and i k coded-bit c that sends symbol _i,kexternal information be expressed as p ^t(c _i,k) and this method to c _i,kc while being approximately Gaussian Profile _i,kexternal information be expressed as

be initialized as,

$p^t\left(c_{i,k}\right)=\frac{1}{2},$

${\tilde{p}}^t\left(c_{i,k}\right)=\frac{1}{2};$

Step (2), propagate and renewal external information:

In the iterations requiring, in the t time iterative process, N is total number of users, carries out following steps:

Step (2.1), for numbering i, has 0 < i < N+1,

represent to send symbol node x _ibe delivered to observation symbol node f _jexternal information, be

an approximate Gaussian Profile,

average and variance be expressed as with

χ represents the symbol field of QPSK, carries out following steps and upgrades

with

${\hat{x}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{\mathrm{\&alpha;}}_i{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

${\widehat{\mathrm{\&tau;}}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{|{\mathrm{\&alpha;}}_i-{\hat{x}}_{x_i}^t|}^2{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

Step (2.2), based on after upgrading

with

for numbering j, there is 0 < j < N+1, h _i,jthe coefficient that represents channel transfer matrix Η,

for the variance of noise, y _jbe j receiving symbol, upgrade

with

${\mathrm{\&tau;}}_{f_j}^t={\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA0000403142010000041.png"\; state="\{\{state\}\}">n</figure-callout>}}^2+\underset{k}{\mathrm{\&Sigma;}}{\left|h_{j,k}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_k}^t,$

$z_{f_j}^t=y_j-\underset{k}{\mathrm{\&Sigma;}}{h}_{j,k}{\hat{x}}_{x_k,}^t$

Meanwhile, for same j, for numbering i, there is 0 < i < N+1, will

be approximately a Gaussian Profile

after, average

and variance

be respectively,

${\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t={\mathrm{\&tau;}}_{f_j}^t-{\left|h_{j,i}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_i,}^t$

$z_{f_j\&RightArrow;x_i}^t=z_{f_j}^t+h_{j,i}{\hat{x}}_{x_i}^t,$

Step (2.3), for numbering i, has 0 < i < N+1, calculates x _iaverage in iteration MMSE detection algorithm

and variance

${\mathrm{\&gamma;}}_{x_i}^t\&LeftArrow;{\left(\underset{j}{\mathrm{\&Sigma;}}\frac{{\left|h_{j,i}\right|}^2}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t}\right)}^{-1},$

${\mathrm{\&zeta;}}_{x_i}^t\&LeftArrow;{\mathrm{\&gamma;}}_{x_i}^t\underset{j}{\mathrm{\&Sigma;}}\frac{h_{j,i}^{*}{z}_{f_j\&RightArrow;x_i}^t}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t},$

Meanwhile, for numbering q, there is 0 < q < Q+1, calculate coded-bit c _i,qexternal information

Step (3), utilizes decoder completes iterative process one time:

To l=1,2 ... L, q=1,2 ... Q, the coded-bit c of generation next iteration _i,qexternal information,

${\tilde{p}}^{t+1}\left(c_{i,q}\right[l\left]\right),p^{t+1}\left(c_{i,q}\right[l\left]\right);$

Step (4), repeats above step until meet iterations, output decode results.

Associating Multiuser Detection based on belief propagation algorithm in the present invention is compared with traditional multi-user interference eliminating method with interpretation method has following two notable features:

● jointly eliminate and disturb;

● disturb and eliminate and decoding Combined Treatment;

Respectively these two features are described below:

● jointly eliminate and disturb;

Associating Multiuser Detection based on belief propagation algorithm is different with traditional multiuser detection in the algorithm principle of Multiuser Detection part with in interpretation method.Traditional Multi-User Detection method be serial interference elimination or parallel interference to eliminate every iteration be all once to eliminate and to rebuild, and associating Multiuser Detection based on belief propagation algorithm and interpretation method can realize all users and side by side jointly disturb and eliminate.

● disturb and eliminate and decoding Combined Treatment;

As shown in Figure 2, the present invention contains " coding/decoding node ", " mapping/anti-mapping node " and " Multiuser Detection node ", due to the feature of this algorithm be external information in each node and each internodal propagation, three nodes closely link together and form the entire infrastructure of algorithm.The interference that the present invention realizes between different user at Multiuser Detection node is eliminated, and at coding and decoding node, realizes decoding, has realized disturbing and has eliminated and decoding Combined Treatment.

Accompanying drawing explanation

The associating Multiuser Detection of Fig. 1 based on belief propagation algorithm and the factor graph of interpretation method composition;

Associating Multiuser Detection and the interpretation method algorithm flow chart of Fig. 2 based on belief propagation algorithm;

Associating Multiuser Detection and the interpretation method receiver schematic diagram of Fig. 3 based on belief propagation algorithm;

The associating Multiuser Detection of Fig. 4 based on belief propagation algorithm and interpretation method and the Performance Ratio of MMSE decoding algorithm under RICE channel are;

Embodiment

As shown in Figure 2, associating Multiuser Detection and interpretation method that the present invention proposes comprise the following steps for the associating Multiuser Detection based on belief propagation algorithm that the present invention proposes and interpretation method:

When this method starts, the first external information of initialization top " coding/decoding node " as shown in Figure 2, the corresponding relation of information bit and coded-bit depends on coded system.The symbol sebolic addressing receiving is input to " the Multiuser Detection node " shown in accompanying drawing 1, and initialization associating Multiuser Detection and code translator, now arrange iterations and be initialized as 0.Then, do not meeting when iterations condition is set, Multiuser Detection node carries out special delivery, calculating and the renewal of external information based on belief propagation algorithm; After meeting iterations, as shown in Figure 2, the external information of the coded-bit that " Multiuser Detection node " output obtains, through " mapping/anti-mapping node ", " coding/decoding node " obtains decode results.

Principle and the arthmetic statement of the method for the invention are as follows:

1) initialization:

Iterative algorithm initial phase t=1, arranges,

${\mathrm{\&zeta;}}_{x_i}^{t-1}=0,{\mathrm{\&gamma;}}_{x_i}^{t-1}=1,$

$p^t\left(c_{i,k}\right)=\frac{1}{2},$

${\tilde{p}}^t\left(c_{i,k}\right)=\frac{1}{2};$

2) propagation and renewal external information:

In the t time iterative process, carry out following steps:

A), for numbering i, carry out following steps and upgrade

with

${\hat{x}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{\mathrm{\&alpha;}}_i{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

${\widehat{\mathrm{\&tau;}}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{|{\mathrm{\&alpha;}}_i-{\hat{x}}_{x_i}^t|}^2{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

B) based on after upgrading with

for numbering j, there is 0 < j < N+1, upgrade

with

${\mathrm{\&tau;}}_{f_j}^t={\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="n"\; filenames="HDA0000403142010000041.png"\; state="\{\{state\}\}">n</figure-callout>}}^2+\underset{k}{\mathrm{\&Sigma;}}{\left|h_{j,k}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_k}^t,$

$z_{f_j}^t=y_j-\underset{k}{\mathrm{\&Sigma;}}{h}_{j,k}{\hat{x}}_{x_k,}^t$

Meanwhile, for same j, for numbering i, there is 0 < i < N+1, upgrade

and variance

${\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t={\mathrm{\&tau;}}_{f_j}^t-{\left|h_{j,i}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_i,}^t$

$z_{f_j\&RightArrow;x_i}^t=z_{f_j}^t+h_{j,i}{\hat{x}}_{x_i}^t,$

C) for numbering i, there is 0 < i < N+1, calculate and variance

${\mathrm{\&gamma;}}_{x_i}^t\&LeftArrow;{\left(\underset{j}{\mathrm{\&Sigma;}}\frac{{\left|h_{j,i}\right|}^2}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t}\right)}^{-1},$

${\mathrm{\&zeta;}}_{x_i}^t\&LeftArrow;{\mathrm{\&gamma;}}_{x_i}^t\underset{j}{\mathrm{\&Sigma;}}\frac{h_{j,i}^{*}{z}_{f_j\&RightArrow;x_i}^t}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t},$

Meanwhile, for numbering q, there is 0 < q < Q+1, calculate coded-bit c _i,qexternal information

3) utilize

complete decode procedure:

To l=1,2 ... L, q=1,2 ... Q, the coded-bit c of generation next iteration _i,qexternal information,

${\tilde{p}}^{t+1}\left(c_{i,q}\right[l\left]\right),p^{t+1}\left(c_{i,q}\right[l\left]\right);$

4) repeat above step until meet iterations, output decode results.

A specific embodiment of the present invention

A kind of associating Multiuser Detection and interpretation method based on belief propagation algorithm that the present invention proposes, the multi-user's cdma spread spectrum communication system of take is example.The reverse link of this multi-user cdma spread spectrum communication system is used L frequency range, and the length of the spreading code that cdma spread spectrum communication system is used is 16, and Installed System Memory is 8 users, and modulation system is QPSK, and coding is used LDPC code, under RICE channel, and K=10.Below provide workflow, be convenient to understand object of the present invention, feature and advantage.

(1) adopt the information bit coding of LDPC code to each user;

(2) each subscriber-coded bit stream carries out after the mapping of QPSK constellation point, forms complex data flow.

(3) output time-domain data flow after complex data flow parallel-serial conversion;

(4) the later data of Base-Band Processing convert through digital-to-analogue (D/A), form continuous analog signal, then pass through up-conversion, are modulated to radio frequency;

(5) finally modulation signal is launched.

(6) radio frequency unit of terminal receives signal, realizes down-conversion and analog to digital conversion, produces digital baseband signal;

(7) the external information substitution Multiuser Detection node a plurality of users' digital baseband information being calculated, is transmitted and is upgraded by belief propagation algorithm, draws the external information of a plurality of user symbols;

(8) external information of the different user symbol upgrading is passed through to " mapping/anti-mapping node ", calculate the external information of each subscriber-coded bit stream;

(9) by the external information of the different user coded-bit upgrading by " coding/decoding node ", calculate the external information of each user profile bit stream;

(10) by the external information of the message bit stream having obtained according to the direction going down of schematic structure, successively pass through " coding/decoding node ", " coding/decoding node " and " Multiuser Detection node ", recalculates and upgrades external information;

(11) repeat said process, until meet iterations.

Said system is carried out to Computer Simulation test, has drawn result below:

Accompanying drawing 4 and table 1, the associating Multiuser Detection based on belief propagation algorithm and interpretation method and the comparison of traditional MMSE ber curve.Can see that the associating Multiuser Detection based on belief propagation algorithm compares with additive method and have the lower error rate under equal signal to noise ratio condition with interpretation method.

The performance of two kinds of decoding algorithms of table 1. under RICE channel

?	E b/N 0＝2dB	E b/N 0＝3dB	E b/N 0＝4dB
				IteraMMSE-QPSK	0.0350	0.00038	0.0005
ABP-QPSK	0.0175	0.0011	0.0001

Visible, adopt this method under identical signal to noise ratio condition, to obtain the lower error rate, improved laser propagation effect.

Effect of the present invention is, by utilizing propagation and the renewal of external information on the factor graph of Multiuser Detection node formation to make external information feature more reliably, effectively lower ambient noise and disturb the impact on signal message, lower error code result and more reliable laser propagation effect have been obtained, this algorithm can be realized multi-user interference elimination and carry out combining of decoding simultaneously, is conducive to the realization of parallel organization.Therefore, for multi-user interference, eliminate, this method is obviously better than additive method.

Claims (1)

1. associating Multiuser Detection and the interpretation method based on belief propagation algorithm is characterised in that, described method detailed process is according to following steps, to realize successively:

Step (1), initialization:

I the symbol sending of first setting in iteration detection method is x _i, x _iaverage and variance the t time iteration are used

with

represent, iterative algorithm initial phase t=1, arranges,

${\mathrm{\&zeta;}}_{x_i}^{t-1}=0,{\mathrm{\&gamma;}}_{x_i}^{t-1}=1,$

Meanwhile, owing to sending, adopt QPSK modulation, a transmission symbol correspondence 2 coded-bits, in the t time iteration, and i k coded-bit c that sends symbol _i,kexternal information be expressed as p ^t(c _i,k) and this method to c _i,kc while being approximately Gaussian Profile _i,kexternal information be expressed as

be initialized as,

$p^t\left(c_{i,k}\right)=\frac{1}{2},$

${\tilde{p}}^t\left(c_{i,k}\right)=\frac{1}{2};$

Step (2), propagate and renewal external information:

In the iterations requiring, in the t time iterative process, N is total number of users, carries out following steps:

Step (2.1), for numbering i, has 0 < i < N+1,

represent to send symbol node x _ibe delivered to observation symbol node f _jexternal information, be an approximate Gaussian Profile,

average and variance be expressed as

with χ represents the symbol field of QPSK, carries out following steps and upgrades

with

${\hat{x}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{\mathrm{\&alpha;}}_i{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

${\widehat{\mathrm{\&tau;}}}_{x_i}^t=\underset{{\mathrm{\&alpha;}}_i\&Element;\mathrm{\&chi;}}{\mathrm{\&Sigma;}}{|{\mathrm{\&alpha;}}_i-{\hat{x}}_{x_i}^t|}^2{\tilde{p}}^t(x_i={\mathrm{\&alpha;}}_i),$

Step (2.2), based on after upgrading

with for numbering j, there is 0 < j < N+1, h _i,jthe coefficient that represents channel transfer matrix Η,

for the variance of noise, y _jbe j receiving symbol, upgrade

with

${\mathrm{\&tau;}}_{f_j}^t={\mathrm{\&sigma;}}_n^2+\underset{k}{\mathrm{\&Sigma;}}{\left|h_{j,k}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_k}^t,$

$z_{f_j}^t=y_j-\underset{k}{\mathrm{\&Sigma;}}{h}_{j,k}{\hat{x}}_{x_k,}^t$

Meanwhile, for same j, for numbering i, there is 0 < i < N+1, will

be approximately a Gaussian Profile

after,

average and variance

be respectively,

${\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t={\mathrm{\&tau;}}_{f_j}^t-{\left|h_{j,i}\right|}^2{\widehat{\mathrm{\&tau;}}}_{x_i,}^t$

$z_{f_j\&RightArrow;x_i}^t=z_{f_j}^t+h_{j,i}{\hat{x}}_{x_i}^t,$

Step (2.3), for numbering i, has 0 < i < N+1, calculates x _iaverage in iteration MMSE detection algorithm

and variance

${\mathrm{\&gamma;}}_{x_i}^t\&LeftArrow;{\left(\underset{j}{\mathrm{\&Sigma;}}\frac{{\left|h_{j,i}\right|}^2}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t}\right)}^{-1},$

${\mathrm{\&zeta;}}_{x_i}^t\&LeftArrow;{\mathrm{\&gamma;}}_{x_i}^t\underset{j}{\mathrm{\&Sigma;}}\frac{h_{j,i}^{*}{z}_{f_j\&RightArrow;x_i}^t}{{\mathrm{\&tau;}}_{f_j\&RightArrow;x_i}^t},$

Meanwhile, for numbering q, there is 0 < q < Q+1, calculate coded-bit c _i,qexternal information

Step (3), utilizes

decoder completes iterative process one time:

To l=1,2 ... L, q=1,2 ... Q, the coded-bit c of generation next iteration _i,qexternal information,

${\tilde{p}}^{t+1}\left(c_{i,q}\right[l\left]\right),p^{t+1}\left(c_{i,q}\right[l\left]\right);$

Step (4), repeats above step until meet iterations, output decode results.

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