WO2006069488A1

WO2006069488A1 - A combination detection method for wireless communication system with antennea array

Info

Publication number: WO2006069488A1
Application number: PCT/CN2004/001583
Authority: WO
Inventors: Xinxing Yang
Original assignee: Zte Corporation
Priority date: 2004-12-31
Filing date: 2004-12-31
Publication date: 2006-07-06
Also published as: KR100992432B1; HK1099613A1; KR20070104570A; JP2008526142A; JP4560088B2; CN101036318B; CN101036318A

Abstract

A combination detection method for wireless communication system with antennae array, include: estimate the channel of each aerial that is from each mobile equipment to antennae array; estimate each mobile equipment's beam shaping weight value; weight the received signal, acquired the data of each mobile equipment after beam shaping; using said data after beam shaping, combination detect each mobile equipment's data. The present invention using beam shaping weight value weighted mode to carry out combination detection, not only can greatly reducing the bad influence of combination detection performance depressed due to signal synchronization between mobile equipment is below the mark, but also decrease multiple access interference owing to channel estimate inaccuracy, at the same time improve combination detective input data SNR, thereby advance combination detection algorithm performance. Furthermore, the present invention is realized simplicity; complexity of computation is also low. Furthermore, the present invention also provides the base station realize the said combination detection method.

Description

Joint detection method and base station for wireless communication system with antenna array

The present invention relates to a receiving technique for a time division duplex (TDD) wireless communication system, and more particularly to a method for joint detection of signals received through an antenna array in a TDD system and a base station implementing the method. Background technique

In a Code Division Multiple Access (CDMA) communication system, all mobile devices simultaneously communicate using different spreading codes on the same frequency. Since there is multipath transmission in the actual wireless signal transmission environment, there is mutual interference between the symbols of continuous transmission of each mobile device, that is, inter-symbol interference (ISI). At the same time, since the mobile radio channel is time-varying, the orthogonality of the spreading codes between different mobile devices cannot be guaranteed at the receiving end, so there is also interference between symbols of different mobile devices, that is, multiple access interference (MAI). .

In mobile communication systems, there are typically two different types of receivers. The first type of receiver is a conventional matched filter or RAKE receiver, which considers the intersymbol interference ISI and the multiple access interference MAI as the noise of the transmitted signal. The RAKE receiver is capable of distinguishing signals from different transmission paths of the same mobile device and combining them according to certain criteria, but the output of the RAKE receiver still contains multiple access interference MAI. For the matched filter, since the orthogonality of the spreading code between the mobile devices cannot be guaranteed at the receiving end, the output result includes both the inter-symbol interference ISI and the multiple access interference MAI. In addition, the use of matched filters does not solve the near-far effect, so the power must be strictly controlled. The second receiver utilizes all information of inter-symbol interference ISI and multiple-access interference MAI, and uses interference cancellation (IC) method or joint detection (JD) method to obtain signals transmitted by mobile devices. The interference cancellation method uses cascading cancellation or parallel cancellation to subtract the multiple access interference MAI signal from the total signal to obtain a "clean" signal for each mobile device. The joint detection method utilizes information of inter-symbol interference ISI and multiple-access interference MAI, and simultaneously detects signals of all mobile devices. The optimal joint detection method is a nonlinear maximum likelihood sequence estimation algorithm, which uses the Viterbi algorithm to detect the transmission sequence of the most likely K mobile devices. The computational complexity of the algorithm is 0 (2. Thus, if the number of mobile devices is large, it is impossible to implement joint detection in real time. In 1993, Klein and Baier proposed a zero-forcing block linear equalization (ZF-BLE). Excellent joint detection algorithm, which greatly reduces the computational complexity. The ZF-BLE algorithm completely cancels the inter-symbol interference ISI and the multiple-access interference MAI, that is, the ISI and MAI values are zero, so the algorithm is zero-forcing and unbiased estimation. However, in the signal results obtained by the ZF-BLE algorithm, the noise is color noise, which will affect the final decision circuit output, and the performance of the ZF-BLE algorithm is degraded. Subsequently, Jung et al. proposed a minimum Mean square error block linear equalization (MMSE-BLE) joint detection algorithm, which uses the MMSE criterion to minimize the error between the estimated value and the true value. This algorithm can be said that the ZF-BLE algorithm passes through a Wiener estimator. The extension, which causes the noise terms in the result to be decorrelated, thus reducing the influence of the color noise term on the decision circuit. Thereafter, Jung et al. also performed the ZF-BLE algorithm and the MMSE-BLE algorithm. The improvement is proposed, and the block decision feedback equalization (ZF-BDFE) joint detection algorithm and the minimum mean square error block decision feedback equalization (MMSE-BDFE) joint detection algorithm are proposed, although the latter two algorithms are better than ZF-BLE algorithm and MMSE-BLE algorithm. The performance is better, but the computational complexity is even higher.

Although the joint detection method is better than the matched filtering method in performance, for the TDD system, the joint detection method is affected by signal synchronization and channel estimation accuracy. If the signal synchronization between mobile devices is not good or the channel estimation is poor, This can seriously affect the performance of the joint detection method, and this effect will increase as the number of mobile devices increases. Therefore, how to overcome the above defects and improve the reliability of the joint detection method is an urgent problem to be solved. Summary of the invention

The present invention has been made in view of the above problems in the prior art, and an object thereof is to provide a new joint detection method for a wireless communication system having an antenna array, which can reduce signal synchronization due to a mobile device. The influence of channel estimation error, and can improve the signal-to-noise ratio of the joint detection input data, especially suitable for the case of a large number of mobile devices.

Another object of the present invention is to provide a base station that implements the above joint detection method. According to an aspect of the present invention, a wireless communication system for an antenna array is provided Joint detection methods, including:

Estimating the channel of each mobile device to each antenna of the antenna array;

Estimating the beamforming of each mobile device.

The received signals are weighted to obtain beamformed data of each mobile device; and the data of each mobile device is jointly detected by using the beamformed data.

Preferably, the step of estimating the beamforming value of each mobile device is implemented by any one of a fixed beam search method, a maximum power method, a maximum signal to interference ratio method, or an adaptive weight estimation method.

Preferably, the step of jointly detecting further comprises:

Generating a composite channel impulse response for each mobile device using the channel estimate and a spread spectrum scrambling code of the mobile device;

Weighting the composite channel impulse response;

Data of each mobile device is detected based on the weighted composite channel impulse response and the data after beamforming of each mobile device.

According to another aspect of the present invention, a base station for implementing the joint detection method is provided, including:

An antenna array, configured to receive a signal transmitted by a mobile device;

a radio frequency transceiver for sampling down-converting a received signal;

a baseband processor for performing baseband processing on the sampled data;

The baseband processor further includes:

a channel estimator for estimating a channel of each mobile device to each antenna; a weight estimator for estimating a beamforming weight of each mobile device according to the estimated channel; and a signal processor for performing the joint Detect, get data for each mobile device.

Preferably, the signal processor further comprises:

a first generator, configured to perform a convolution operation on the channel estimation value to generate a composite channel impulse response;

a second generator, configured to perform weighting on the composite channel impulse response by using the beamforming weight, to generate a force P weight composite channel impulse response; a third generator, configured to generate a system matrix by using the weighted composite channel impulse response; a fourth generator, configured to generate an inverse matrix of the system matrix;

a data weighting unit, configured to perform weighting on the sampled data by using the beamforming weight; a matched filter, configured to perform matched filtering on the system matrix and the weighted data; and a fifth generator, configured to: Generating data for each mobile device based on the inverse matrix and the matched filtering result.

The invention utilizes beamforming weights to perform joint detection, which not only greatly reduces the influence of joint detection performance degradation due to poor signal synchronization between mobile devices, but also reduces the inaccuracy of channel estimation. The multiple access interference (MAI) is added, and the signal-to-noise ratio of the input data of the joint detection is improved, thereby improving the performance of the joint detection algorithm. Moreover, the present invention is simple to implement and has a low computational complexity. DRAWINGS

1 is a flow chart of a joint detection method in accordance with one embodiment of the present invention;

2 is a block diagram showing the structure of a base station implementing the joint detecting method shown in FIG. 1 according to an embodiment of the present invention;

Figure 3 is a schematic diagram of a signal processor of the base station shown in Figure 2. detailed description

The above and other objects, features and advantages of the present invention will become more <RTIgt;

1 is a flow chart of a joint detection method in accordance with one embodiment of the present invention. As shown in FIG. 1, at step 101, a channel of each mobile device to each antenna of the antenna array is estimated using a training sequence. In a wireless communication system, it is often necessary to use a training sequence to estimate a channel. Therefore, in the present embodiment, channel estimation is performed on received data by using a training sequence known to each mobile device, and channels of all mobile devices on all antennas are obtained. estimate. The specific processing is as follows: Assume that there is a mobile device communication in the wireless communication system, the antenna array of the base station includes T antennas, the training sequence of each mobile device is known, and the training sequence of all mobile devices The matrix consisting of columns is M, and the training sequence of all mobile devices received by 笫i antennas in a burst is ^

e. = Mh _t i=l, ..., 7V (1) where Α,. is the vector of the channel estimates of all mobile devices on the ith antenna, ie ^. -[(Α^,... , ^). Solving equation (1), you can get channel estimation

h _i ={M ^H MY ^x M ^H e _i (2)

In the case of a CDMA TDD system, the M matrix of the training sequence of all mobile devices can be written as a cyclic matrix, so the Fast Fourier Transform (FFT) can be used to solve the equation (1):

^ = ( (^)/ (^(:,1))) (3) where M(:,l) represents all elements of the first column of the M matrix.

Since the orientation information of the mobile device relative to the antenna array is already included in the channel estimation, in step 105, the beamforming weight of each mobile device can be calculated using the estimated channel according to the array signal processing method. The beamforming weight can be estimated by any one of a fixed beam search method, a maximum power method, a maximum signal to interference ratio method, or an adaptive weight estimation method. These methods are well known to those skilled in the art.

Then, in step 110, the received data is weighted using the respective beamforming weights of each mobile device to obtain beamformed data for each mobile device. Because the beam has a certain width, and since the beam side lobes may not completely disappear, the beamformed data is not completely data of a mobile device, and it may be interfered by data of other mobile devices. However, even if there is interference, since the beamforming is performed, the interference at this time is much smaller than that before the wave velocity forming, and the signal-to-noise ratio of the data after beamforming is also improved. Specifically, it is assumed that the data portion of the burst received by the ith antenna is the column vector r, ., then the data portion of the kth mobile device beam is formed.

(4)

Where _w represents the weight of the 笫i root antenna of the k mobile devices.

Since beamformed data may also contain interference, beamformed data is utilized Jointly detect the data of each mobile device. Since the interference between the mobile devices has been minimized before the joint detection, and the signal-to-noise ratio of the mobile device data is also improved, the performance of the joint detection can be greatly improved. If the mobile device is not in the same beam, even if the signal synchronization is not very good, there is no impact on the joint detection. If there are beam side lobes or the mobile device is in the same beam, but the intra-beam mobile device has been reduced, the performance of the joint detection can also be improved.

In the present embodiment, the joint detection is performed as follows.

At step 115, the channel estimate for each mobile device on each antenna is convolved with the spread spectrum scrambling code for each mobile device to form a corresponding composite channel impulse response. Assuming a composite channel impulse response of the ith antenna of the kth mobile device, then 6

Wherein, the spreading scrambling code representing the kth mobile device indicates the channel estimation of the ith antenna of the kth mobile device.

At step 116, the composite impulse response of each mobile device is weighted by using the beamforming weights of each mobile device obtained previously to form a weighted composite channel impulse response 3⁄4, m = l, ..., ^/c = l,...,^ , where m and k can be different values, and the expression is

At step 117, the weighted composite channel impulse response is used to form a system matrix Α ^ [(Α ¹ ) ^Τ , ..., (Α ^Κ ) ^Τ ] ^Τ , where '' is from 3⁄4, , /( = 1,. .. , composed of Toeplitz matrix. The beamformed data of each mobile device is arranged into a vector F = [i , ..., F /] Assuming that the vector of symbols transmitted by all mobile devices is rf , then the system equation is

r = Ad + n (6)

If "is Gaussian white noise with variance, then the solution using the least squares algorithm or the maximum likelihood algorithm is

d = (A ^H Ay ^l A ^H r (7)

At the same time, if they are independent and identically distributed, then the minimum mean square solution (MMSE) is

At this point, the data of each mobile device can be obtained through the above steps. As can be seen from the above description, with the embodiment, the influence of the joint detection performance degradation caused by the poor synchronization of the mobile device and the channel estimation error can be reduced, and the signal-to-noise ratio of the input data of the joint detection is improved, thereby improving the joint. Detect the performance of the algorithm.

2 is a block diagram showing the structure of a base station implementing the joint detecting method shown in FIG. 1 according to an embodiment of the present invention. As shown in FIG. 2, the base station includes N identical omnidirectional antenna units.

201A, 201B 201N antenna array, N radio frequency transceivers 203A,

203B, ..., 203N, and baseband processor 204. The antenna units 201A, 201B, ..., 201N receive signals transmitted by the K mobile devices, and output to the corresponding radio frequency transceivers 203A, 203B, ..., 203N for sampling down-conversion. All RF transceivers 203 use the same local oscillator source to ensure that the RF transceivers in the same base station are coherent. Each of the radio frequency transceivers includes an analog-to-digital converter (ADC) such that signals output by all of the radio frequency transceivers 203 to the baseband processor 204 are digital signals between the radio frequency transceivers 203 and the baseband processor 204. Connected via a high speed digital bus. The radio frequency transceiver 203 transmits the sampled data to the baseband processor 204 for baseband processing.

The baseband processor 204 includes N channel estimators 207A, 207B, ..., 207N, a weight estimator 208, and a signal processor 209 corresponding to the N radio frequency transceivers 203A, 203B, ..., 203N. The received data subjected to down-conversion by the RF transceivers 203A, 203B, ..., 203N is output to the channel estimators 207A, 207B, ... 207N for channel estimation, and the channels of all mobile devices on each antenna are used. The estimate / ^..., /^ is output to the weight estimator 208. The weight estimator 208 can calculate the beamforming weights of all mobile devices according to any one of a fixed beam method, a maximum power method, a maximum signal to interference ratio method, or other adaptive algorithms, and then channel estimates h , , h _N , beamforming The weights ^ and the received data ... are output to the signal processor 209 for weighted joint detection to obtain data for each mobile device.

Figure 3 shows a schematic diagram of the signal processor 209 of the base station shown in Figure 2. As shown in FIG. 3, the signal processor 209 includes: a first generator 2092 for generating a composite channel impulse response, a second generator 2093 for generating a weighted composite channel impulse response, and a system for generating a system matrix a third generator 2094, a fourth generator 2095 for generating an inverse matrix of the system matrix, a data weighter 2096 for weighting the received data, , a matched filter 2097, and a data for generating data of the mobile device Five generators 2098.

The channel estimate output by the channel estimator 207 is input to the first generator 2092, and is convoluted with the respective spread spectrum scrambling codes of each mobile device to generate respective composite channel impulses bi = i, ..., N, k = \,...,K. Then, the composite channel impulse response is weighted in the second generator 2093 using the beamforming weight ^A ' from the weight estimator 208 to produce a weighted composite channel impulse response, = 1, ..., = 1, .. . , . The third generator 2094 generates a system matrix using a weighted composite channel impulse response, a system matrix) ⁷ , . . . , ^^) ⁷ ^ , where is a Toeplitz matrix composed of m, t = i, . The system matrix ^ (inverse matrix 04^4)-' is then generated in the fourth generator 2095. The data weighter 2096 weights the received data from the radio frequency transceiver 203, ..., using the beamforming weights output by the weight estimator 208, generates weighted received data, and arranges them into vectors. The third generator 2094 outputs a system matrix ^ (and the data weighter 2096 outputs the received data vector F to the matched filter 2097 for matched filtering, and outputs the result of the matched filtering to the fifth generator 2098. In the fifth generator 2098 The data output of each mobile device is generated using a least squares algorithm or a maximum likelihood algorithm or a minimum mean square error algorithm.

As can be seen from the above description, by using the base station of this embodiment, weighted joint detection can be implemented, and the effect of joint detection performance degradation caused by poor signal synchronization of the mobile device and channel estimation error is reduced, and the performance of the joint detection is improved.

Claims

Claim

A joint detection method for a wireless communication system having an antenna array, characterized by comprising:

Estimating beamforming weights for each mobile device;

2. The joint detection method according to claim 1, wherein the step of estimating a beamforming weight of each mobile device adopts a fixed beam search method, a maximum power method, a maximum signal to interference ratio method, or an adaptive right. Any one of the value estimation methods.

3. The joint detection method according to claim 1, wherein the step of jointly detecting further comprises:

Weighting the composite channel impulse response;

The data of each mobile device is detected based on the weighted composite channel impulse response and the data after beamforming of each mobile device.

4. The joint detection method according to claim 3, wherein the step of detecting data of each mobile device further comprises:

The weighted composite channel impulse response is used to form the system matrix ^ = ) ^r , ..., ( ) ^r , where is the 1 ^ 1 ^ matrix composed of the weighted composite channel impulse response, m, ^ 1,..., ^fi: is the total number of mobile devices;

The beamformed data of each device is arranged into a vector F = ,..., ;f, assuming that the vector of data of all mobile devices is rf, then the system equation is r = Ad + n

If "is a variance. Gaussian white noise, then = (Α ^Η Α)- ^ι Α ^Η ΐ;

If it is independent and identically distributed, then = (A ^H A + σΙΐγ ^ι Α ^Η ν.

A base station for implementing the joint detection method according to any one of claims 1 to 4, comprising: An antenna array, configured to receive a signal transmitted by a mobile device;

a radio frequency transceiver for sampling down-converting a received signal;

a baseband processor for performing baseband processing on the sampled data;

The baseband processor further includes:

The base station according to claim 5, wherein the signal processor further comprises:

a second generator, configured to weight the composite channel impulse response by using the beamforming weight to generate a weighted composite channel impulse response;

a third generator, configured to generate a system matrix by using the weighted composite channel impulse response; a fourth generator, configured to generate an inverse matrix of the system matrix;