CN102723975B - Signal detection method and device of MIMO (multiple input multiple output) system - Google Patents

Signal detection method and device of MIMO (multiple input multiple output) system Download PDF

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CN102723975B
CN102723975B CN201210191954.6A CN201210191954A CN102723975B CN 102723975 B CN102723975 B CN 102723975B CN 201210191954 A CN201210191954 A CN 201210191954A CN 102723975 B CN102723975 B CN 102723975B
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毛新宇
吴建军
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Peking University
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Abstract

本发明涉及信号检测技术领域,公开了一种MIMO系统的信号检测方法及装置,方法包括:S1、利用已知的信道矩阵构造搜索树;S2、对搜索树的第N层进行搜索;S21、将K个节点分为M组;S22、按照每组内节点的展开子节点数相等、不同组内节点的展开子节点数从M开始依次递减的原则,展开第N层节点的子节点;S23、按照所有展开的子节点对应的部分接收矢量与部分发送矢量之间的欧氏距离进行排序,保留欧氏距离小于阈值的K个子节点用于下一层的搜索;S3、按照步骤S21~S23的方式将所有层都搜索完,在由子节点组成的路径中寻找欧氏距离最小的路径作为搜索结果。本发明降低了信号检测的复杂度,提高了接收数据的检测效率。

The present invention relates to the technical field of signal detection, and discloses a signal detection method and device for a MIMO system. The method includes: S1, using a known channel matrix to construct a search tree; S2, searching the Nth layer of the search tree; S21, Divide the K nodes into M groups; S22, according to the principle that the number of expanded child nodes of nodes in each group is equal, and the number of expanded child nodes of nodes in different groups decreases sequentially from M, expand the child nodes of the Nth layer node; S23 1. Sorting according to the Euclidean distance between the part of the received vector and the part of the sent vector corresponding to all expanded sub-nodes, and keeping K sub-nodes whose Euclidean distance is smaller than the threshold for the search of the next layer; S3, according to steps S21-S23 All layers are searched in the same way, and the path with the smallest Euclidean distance is found in the path composed of child nodes as the search result. The invention reduces the complexity of signal detection and improves the detection efficiency of received data.

Description

MIMO系统的信号检测方法及装置Signal detection method and device for MIMO system

技术领域 technical field

本发明涉及信号检测技术领域,特别是涉及一种MIMO系统的信号检测方法及装置。The present invention relates to the technical field of signal detection, in particular to a signal detection method and device for a MIMO system.

背景技术 Background technique

多输入多输出(MIMO)技术可以在不增加频带的前提下,成倍地提高传输速率,现今频率资源日益紧张,所以,MIMO技术被认为是下一代宽带无线通信技术中最为重要的物理层技术之一。Multiple-input multiple-output (MIMO) technology can double the transmission rate without increasing the frequency band. Today, frequency resources are increasingly tight. Therefore, MIMO technology is considered to be the most important physical layer technology in the next generation of broadband wireless communication technology. one.

在MIMO系统中,为了提高信源信息传输的可靠性,在发送端,待传输信号首先经过能够提供纠错能力的信道编码,再进行空时/空频/空时频编码,然后由多幅发送天线同时或者按照一定的时间顺序发送出去。在接收端,由多幅接收天线同时或者按照一定的时间顺序接收来自于发送端的信号,并依次进行空时/空频/空时频解码和信道译码,从而将译码结果作为待发送信号的原始信息。In a MIMO system, in order to improve the reliability of source information transmission, at the sending end, the signal to be transmitted is firstly subjected to channel coding that can provide error correction capabilities, and then space-time/space-frequency/space-time-frequency coding, and then multiple The transmitting antennas transmit at the same time or in a certain time sequence. At the receiving end, the signals from the sending end are received by multiple receiving antennas simultaneously or in a certain time sequence, and space-time/space-frequency/space-time-frequency decoding and channel decoding are performed in sequence, so that the decoding result is used as the signal to be sent original information.

接收端所进行的信道译码实质上是对接收信号的检测,即从接收信号中检测出最优信号,作为译码结果。然而,MIMO技术的信号检测技术却面临着巨大的难题,虽然最大似然(ML)算法从最小错误概率的意义上看是最优的,但是其计算复杂度大,是天线数目及调制阶数的指数形式,例如:采用16QAM(16阶正交幅度调制)方式调制、共有5幅天线时,计算复杂度为165=1048576。实时系统难以接受这样大的计算复杂度。The channel decoding performed by the receiving end is essentially the detection of the received signal, that is, the optimal signal is detected from the received signal as the decoding result. However, the signal detection technology of MIMO technology is facing a huge problem. Although the maximum likelihood (ML) algorithm is optimal in the sense of minimum error probability, its computational complexity is large, which is caused by the number of antennas and the modulation order. The exponential form of , for example, when 16QAM (16-order quadrature amplitude modulation) modulation is adopted and there are 5 antennas in total, the calculation complexity is 16 5 =1048576. It is difficult for real-time systems to accept such a large computational complexity.

作为ML算法的一种近似简化,球形译码(Sphere Decoding,SD)系列算法因为其接近于或等于ML的性能以及相比于ML算法大大降低的计算复杂度日益受到广泛关注。因此,球形译码方式成为当前MIMO信号检测的首选方案。As an approximate simplification of the ML algorithm, the Sphere Decoding (SD) series of algorithms has attracted increasing attention because of its performance close to or equal to ML and its greatly reduced computational complexity compared to ML algorithms. Therefore, the sphere decoding method has become the preferred solution for MIMO signal detection at present.

在球形译码方式中有深度优先算法(Depth First SD,DFSD)、距离优先算法(Metric First SD,MFSD)和宽度优先球形译码(BreadthFirst SD,BFSD)算法三种子算法,其中宽度优先算法又称为K-bestSD算法,以下采用业界通用的简称K-best SD。其中,宽度优先算法因为计算复杂度固定,计算时间确定,在实时系统中有极大的应用前景。然而,宽度优先算法的计算复杂度仍然很大,如何进一步降低复杂度,而且性能基本不受影响,是其应用中的一个热点问题。In the sphere decoding method, there are three sub-algorithms: Depth First SD, DFSD, Metric First SD, MFSD, and Breadth First SD, BFSD. It is called the K-bestSD algorithm, and the industry-wide abbreviation K-best SD is used below. Among them, the breadth-first algorithm has great application prospects in real-time systems because of its fixed computational complexity and definite computational time. However, the computational complexity of the breadth-first algorithm is still very large, how to further reduce the complexity without affecting the performance is a hot issue in its application.

目前已有一些对K-best SD算法的简化方法,Luis G.Barbero等人提出每个节点展开的节点数受限的FSD算法(L.G.Barbero and J.S.Thompson,“A Fixed-Complexity MIMO Detector Based on the ComplexSphere Decoder,”2006IEEE 7th Workshop on Signal ProcessingAdvances in Wireless Communications,Cannes,France:2006,pp.1-5.),一定程度上减少了译码过程中访问的节点,但是只在所保留路径数较多的时候才比较接近K-best SD算法的性能,而此时计算的复杂度比较高。At present, there are some simplified methods for the K-best SD algorithm. Luis G.Barbero et al. proposed an FSD algorithm with a limited number of nodes expanded by each node (L.G.Barbero and J.S.Thompson, "A Fixed-Complexity MIMO Detector Based on the ComplexSphere Decoder, "2006IEEE 7th Workshop on Signal Processing Advances in Wireless Communications, Cannes, France: 2006, pp.1-5.), to a certain extent reduces the nodes visited during the decoding process, but only when the number of reserved paths is large Only when it is close to the performance of the K-best SD algorithm, and at this time, the computational complexity is relatively high.

Cong Xiong等人提出将FSD算法中保留节点数随搜索层数的增加而减少的算法(Cong Xiong,Xin Zhang,Kai Wu,and Dacheng Yang,“A simplified fixed-complexity sphere decoder for V-BLAST systems,”Communications Letters,IEEE,vo1.13,2009,pp.582-584.),这种算法没有FSD足够的减少保留节点数目的理论依据,减少的访问节点数相对有限。Cong Xiong et al proposed an algorithm that reduces the number of reserved nodes in the FSD algorithm as the number of search layers increases (Cong Xiong, Xin Zhang, Kai Wu, and Dacheng Yang, "A simplified fixed-complexity sphere decoder for V-BLAST systems, "Communications Letters, IEEE, vo1.13, 2009, pp.582-584.), this algorithm does not have enough theoretical basis for FSD to reduce the number of reserved nodes, and the reduced number of visited nodes is relatively limited.

发明内容 Contents of the invention

(一)要解决的技术问题(1) Technical problems to be solved

本发明要解决的技术问题是:如何提供一种MIMO系统的信号检测方案,以在基本不降低检测性能的前提下,减少信号检测算法的复杂度,提高接收数据的检测效率。The technical problem to be solved by the present invention is: how to provide a signal detection scheme for a MIMO system, so as to reduce the complexity of the signal detection algorithm and improve the detection efficiency of received data without substantially reducing the detection performance.

(二)技术方案(2) Technical solution

为了解决上述技术问题,本发明提供一种MIMO系统的信号检测方法,包括以下步骤:In order to solve the above-mentioned technical problems, the present invention provides a signal detection method of a MIMO system, comprising the following steps:

S1、利用已知的信道矩阵和可能的发送符号构造搜索树;S1. Construct a search tree by using the known channel matrix and possible transmission symbols;

S2、按照步骤S21~S23对所述搜索树的第N层进行搜索,N为所述搜索树的总层数:S2. Search the Nth layer of the search tree according to steps S21 to S23, where N is the total number of layers of the search tree:

S21、将当前保留节点分为M组,每个节点对应一个发送符号的一部分元素组成的部分发送矢量,所述发送符号表示为发送矢量的形式;S21. Divide the currently reserved nodes into M groups, each node corresponds to a partial transmission vector composed of a part of elements of a transmission symbol, and the transmission symbol is expressed in the form of a transmission vector;

S22、按照每组内节点的展开子节点数相等、不同组内节点的展开子节点数从M开始依次递减的原则,展开第N层节点的子节点,展开子节点的方式为在所述节点所对应的部分发送矢量后面添加新的元素,直到形成整个发送矢量,所述子节点对应所述新的元素;S22. According to the principle that the number of expanded child nodes of nodes in each group is equal, and the number of expanded child nodes of nodes in different groups decreases sequentially from M, expand the child nodes of the Nth layer node. The way to expand the child nodes is to Adding a new element behind the corresponding part of the sending vector until the entire sending vector is formed, and the child node corresponds to the new element;

S23、按照所有展开的子节点对应的部分发送矢量与部分接收矢量之间的部分欧氏距离进行排序,保留部分欧氏距离最小的K个子节点用于下一层的搜索,所述部分接收矢量由与所述发送符号对应的接收符号的一部分元素组成,所述接收符号表示为接收矢量的形式,N、K、M均为正整数;S23. Sorting according to the partial Euclidean distances between the partial sending vectors and partial receiving vectors corresponding to all expanded child nodes, and retaining K child nodes with the smallest partial Euclidean distances for the search of the next layer, the partial receiving vectors It consists of a part of elements of a received symbol corresponding to the sent symbol, the received symbol is expressed in the form of a received vector, and N, K, and M are all positive integers;

S3、按照步骤S21~S23的方式搜索所述搜索树的其它层,在由子节点组成的路径中寻找欧氏距离最小的路径作为搜索结果。S3. Search other layers of the search tree in the manner of steps S21-S23, and search for a path with the smallest Euclidean distance among paths composed of child nodes as a search result.

优选地,步骤S1具体为:将所述信道矩阵的每一行进行排序,然后将排序之后得到的信道矩阵利用所述可能的发送符号三角化以构成所述搜索树。Preferably, step S1 specifically includes: sorting each row of the channel matrix, and then triangulating the channel matrix obtained after sorting by using the possible transmission symbols to form the search tree.

优选地,所述可能的发送符号选自由每个天线可能发送信号的所有排列组合所构成的集合。Preferably, the possible transmission symbols are selected from a set formed by all permutations and combinations of possible transmission signals of each antenna.

优选地,所述将信道矩阵三角化的方式为对信道矩阵进行QR分解。Preferably, the manner of triangulating the channel matrix is performing QR decomposition on the channel matrix.

优选地,步骤S1中,将所述信道矩阵的每一行进行排序的方式是:如果该行所有节点数小于或等于K,则按照增益从小到大的顺序进行排序,否则按照增益从大到小的顺序进行排序。Preferably, in step S1, the method of sorting each row of the channel matrix is: if the number of all nodes in the row is less than or equal to K, then sort in order of gain from small to large, otherwise in order of gain from large to small sorted in order.

优选地,M的初值为发送符号的数目,且M的值随着搜索层数的增多而减小。Preferably, the initial value of M is the number of transmitted symbols, and the value of M decreases as the number of search layers increases.

优选地,步骤S4中由子节点组成路径的方式为:从每一层中选择一个子节点依次连接起来组成2N×m条路径,且所选择的第j层的子节点都是从上一层被选择子节点所展开的,j=2,...,N,m为发送符号的调制阶数。Preferably, in step S4, the path is composed of subnodes: select a subnode from each layer and connect them in turn to form 2 N×m paths, and the selected subnodes of the jth layer are all from the previous layer Expanded by the selected child node, j=2, . . . , N, m is the modulation order of the transmitted symbol.

优选地,将步骤S23中保留部分欧氏距离最小的K个子节点用于下一层的搜索的步骤替换为:保留部分欧式距离最小的K个子节点,并从所述K个子节点中删除欧式距离值大于距离阈值的子节点,剩下的子节点用于下一层的搜索。Preferably, the step of reserving the K child nodes with the smallest partial Euclidean distance for the search of the next layer in step S23 is replaced by: retaining the K child nodes with the smallest partial Euclidean distance, and deleting the Euclidean distance from the K child nodes The child nodes whose value is greater than the distance threshold, and the remaining child nodes are used for the search of the next layer.

优选地,K的数目随着搜索层数的增多而减小。Preferably, the number of K decreases as the number of search layers increases.

本发明还提供了一种MIMO系统的信号检测装置,包括:The present invention also provides a signal detection device for a MIMO system, including:

信号接收单元,用于接收符号;a signal receiving unit, configured to receive symbols;

信号检测单元,用于首先利用已知的信道矩阵和可能的发送符号处理所述信号接收单元接收到的符号以构成搜索树,并按照以下方式对所述搜索树的第N层进行搜索:The signal detection unit is configured to first process the symbols received by the signal receiving unit by using the known channel matrix and possible transmission symbols to form a search tree, and search the Nth layer of the search tree in the following manner:

将当前保留节点分为M组,每个节点对应一个发送符号的一部分元素组成的部分发送矢量,所述发送符号号表示为发送矢量的形式;Dividing the currently reserved nodes into M groups, each node corresponds to a partial transmission vector composed of a part of elements of a transmission symbol, and the transmission symbol number is expressed in the form of a transmission vector;

按照每组内节点的展开子节点数相等、不同组内节点的展开子节点数从M开始依次递减的原则,展开第N层节点的子节点,展开子节点的方式为在所述节点所对应的部分发送矢量后面添加新的元素,直到形成整个发送矢量,所述子节点对应所述新的元素,N为所述搜索树的总层数;According to the principle that the number of expanded child nodes of nodes in each group is equal, and the number of expanded child nodes of nodes in different groups decreases sequentially from M, the child nodes of the Nth layer node are expanded. Add new elements behind the part of the sending vector until the entire sending vector is formed, the child nodes correspond to the new elements, and N is the total number of layers of the search tree;

其次按照所有展开的子节点对应的部分发送矢量与部分接收矢量之间的部分欧氏距离进行排序,保留部分欧氏距离最小的K个子节点用于下一层的搜索,所述部分接收矢量由与所述发送符号对应的接收符号的一部分元素组成,所述接收符号表示为接收矢量的形式,N、K、M均为正整数;Secondly, sort according to the partial Euclidean distance between the partial sending vector and the partial receiving vector corresponding to all expanded sub-nodes, keep the K sub-nodes with the smallest partial Euclidean distance for the search of the next layer, and the partial receiving vector is determined by Composed of a part of elements of a received symbol corresponding to the sent symbol, the received symbol is expressed in the form of a received vector, and N, K, and M are all positive integers;

再次搜索所述搜索树的其它层,在由子节点组成的路径中寻找欧氏距离最小的路径作为搜索结果;Search the other layers of the search tree again, and find the path with the smallest Euclidean distance in the path composed of child nodes as the search result;

信号输出单元,用于将所述信号检测单元得到的搜索结果输出。A signal output unit, configured to output the search result obtained by the signal detection unit.

(三)有益效果(3) Beneficial effects

上述技术方案具有如下优点:通过对搜索树每层搜索时展开节点数进行限制的方法来减少搜索的节点数,在基本不降低检测性能的基础上,降低了信号检测的复杂度,提高了接收数据的检测效率,而且其硬件实现电路更加简单,电路面积更小、功耗更低。本发明的方法还可应用于多用户信号检测及其它信号检测。The above-mentioned technical solution has the following advantages: the number of nodes to be searched is reduced by limiting the number of expanded nodes in each layer of the search tree, and the complexity of signal detection is reduced without substantially reducing the detection performance, and the reception is improved. The detection efficiency of the data is improved, and its hardware implementation circuit is simpler, the circuit area is smaller, and the power consumption is lower. The method of the present invention can also be applied to multi-user signal detection and other signal detection.

附图说明 Description of drawings

图1是根据本发明实施例的信号检测方法的流程图;Fig. 1 is a flowchart of a signal detection method according to an embodiment of the present invention;

图2是根据本发明实施例MIMO系统的发送接收结构示意图;FIG. 2 is a schematic diagram of a transmitting and receiving structure of a MIMO system according to an embodiment of the present invention;

图3是最大似然搜索的搜索树的示意图;Fig. 3 is the schematic diagram of the search tree of maximum likelihood search;

图4是根据本发明实施例对接受到的信号处理后构成的搜索树的示意图;4 is a schematic diagram of a search tree formed after processing received signals according to an embodiment of the present invention;

图5是根据本发明的一个实施例的仿真实验的第一结果图;Fig. 5 is the first result figure of the simulation experiment according to an embodiment of the present invention;

图6是根据本发明的一个实施例的仿真实验的第二结果图。Fig. 6 is a second result diagram of a simulation experiment according to an embodiment of the present invention.

具体实施方式 Detailed ways

下面结合附图和实施例,对本发明的具体实施方式作进一步详细描述。以下实施例用于说明本发明,但不用来限制本发明的范围。The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

图1所示为本发明实施例MIMO系统的信号检测方法的流程图,如图1所示,所述方法包括以下步骤:FIG. 1 is a flowchart of a signal detection method for a MIMO system according to an embodiment of the present invention. As shown in FIG. 1, the method includes the following steps:

S1、处理接收到的信号利用已知的信道矩阵和可能的发送符号以构成搜索树;S1. Process the received signal and use the known channel matrix and possible transmission symbols to form a search tree;

具体为:将所述信道矩阵的每一行进行排序,然后将排序之后的信道矩阵利用所述可能的发送符号三角化以构成搜索树,排序方法是从小到大(如果该层所有可能的节点数不大于K)/增益从大到小(如果该层所有可能的节点数大于K),排序可以对搜索起到加速作用,进一步提高信号检测的速度。所述可能的发送符号选自由每个天线可能发送信号的所有排列组合所构成的集合。Specifically: each row of the channel matrix is sorted, and then the sorted channel matrix is triangulated using the possible transmission symbols to form a search tree, and the sorting method is from small to large (if all possible node numbers in this layer not greater than K)/gain from large to small (if the number of all possible nodes in this layer is greater than K), sorting can speed up the search and further increase the speed of signal detection. The possible transmission symbols are selected from a set formed by all permutations and combinations of possible transmission signals of each antenna.

S2、按照如下步骤对所述搜索树的第N层进行搜索,N为所述搜索树的总层数;S2. Search the Nth layer of the search tree according to the following steps, where N is the total number of layers of the search tree;

S21、将K个当前保留节点分为M组,每个节点对应一个发送符号的一部分元素组成的部分发送矢量,所述发送符号表示为发送矢量的形式,M的初值可以设定为调制星座图的点数,也就是所有发送的信号的数目;每层的组数M不相等,M的数目随着搜索层数的增多而减小,减小可以线性的(如每层M数减小固定的数目),也可以非线性的(如前几层M不变,后几层M直接减小到1);每层的组数不一样,各层中包括子节点最多的组的子节点的数目也可以不一样,可以根据性能及复杂度的要求作取舍折衷。S21. Divide the K currently reserved nodes into M groups, each node corresponds to a partial transmission vector composed of a part of elements of a transmission symbol, the transmission symbol is expressed in the form of a transmission vector, and the initial value of M can be set as a modulation constellation The number of points in the graph, that is, the number of all sent signals; the number of groups M in each layer is not equal, and the number of M decreases as the number of search layers increases, and the reduction can be linear (such as the number of M in each layer decreases fixed number), it can also be non-linear (for example, M in the first few layers is unchanged, and M in the next few layers is directly reduced to 1); the number of groups in each layer is different, and the number of child nodes of the group with the most child nodes in each layer The number can also be different, and a trade-off can be made according to the requirements of performance and complexity.

S22、按照每组内节点的展开子节点数相等、不同组内节点的展开子节点数从M开始依次递减的原则,展开第N层节点的子节点,展开子节点的方式为在所述节点所对应的部分发送矢量后面添加新的元素,直到形成整个发送矢量,所述子节点对应所述新的元素;S22. According to the principle that the number of expanded child nodes of nodes in each group is equal, and the number of expanded child nodes of nodes in different groups decreases sequentially from M, expand the child nodes of the Nth layer node. The way to expand the child nodes is to Adding a new element behind the corresponding part of the sending vector until the entire sending vector is formed, and the child node corresponds to the new element;

S23、按照所有展开的子节点对应的部分发送矢量与部分接收矢量(与所述部分接收矢量对应)之间的部分欧氏距离进行排序,保留部分欧氏距离小于阈值的K个子节点用于下一层的搜索,所述部分接收矢量由与所述发送符号对应的接收符号的一部分元素组成,所述接收符号表示为接收矢量的形式,N、K、M均为正整数;K的数目随着搜索层数的增多而减小;阈值可以根据噪声分布得到;可以将步骤S23中保留部分欧氏距离最小的K个子节点用于下一层的搜索的步骤替换为:保留部分欧式距离最小的K个子节点,并从所述K个子节点中删除欧式距离值大于距离阈值的子节点,剩下的子节点用于下一层的搜索。S23. Sorting according to the partial Euclidean distances between the partial sending vectors and the partial receiving vectors (corresponding to the partial receiving vectors) corresponding to all expanded sub-nodes, and retaining K sub-nodes whose partial Euclidean distances are smaller than the threshold for the next step One layer of search, the part of the receiving vector is composed of a part of the elements of the receiving symbol corresponding to the sending symbol, the receiving symbol is expressed as a receiving vector, and N, K, and M are all positive integers; the number of K varies with Decrease as the number of search layers increases; the threshold can be obtained according to the noise distribution; the step of reserving the K subnodes with the smallest Euclidean distance in step S23 for the search of the next layer can be replaced by: retaining the K subnodes with the smallest Euclidean distance K child nodes, and delete the child nodes whose Euclidean distance value is greater than the distance threshold from the K child nodes, and the remaining child nodes are used for the search of the next layer.

S3、按照步骤S21~S23的方式将所有层都搜索完,在由子节点组成的路径中寻找欧氏距离最小的路径作为搜索结果。S3. Search all the layers according to steps S21-S23, and find the path with the smallest Euclidean distance among the paths composed of child nodes as the search result.

下面对本发明进行更详细的说明。The present invention will be described in more detail below.

1、接收信号的数学表示1. Mathematical representation of received signal

对于MIMO系统,在具有丰富散射路径的条件下,假定发送天线数为Nt,接收天线数为Nr,满足Nt≥Nr,系统模型如图3所示。为了表述方便,取Nt=Nr=N。s=(s1 s2…sN)T是复数的发送矢量,y=(y1 y2…yN)T是复数的接收矢量,n=(n1 n2…nN)T是复数的噪声矢量,满足实部虚部方差为σ,H是N×N阶的复信道矩阵,其中每一个元素都是独立同分布的复高斯随机变量,满足E(|hi,j|2)=1。则接收信号可以表示为:For the MIMO system, under the condition of abundant scattering paths, it is assumed that the number of transmitting antennas is Nt, and the number of receiving antennas is Nr, satisfying Nt≥Nr. The system model is shown in Figure 3. For the convenience of expression, take Nt=Nr=N. s=(s 1 s 2 …s N ) T is a complex sending vector, y=(y 1 y 2 …y N ) T is a complex receiving vector, n=(n 1 n 2 …n N ) T is a complex The noise vector of which satisfies the variance of the real part and the imaginary part is σ, and H is a complex channel matrix of order N×N, in which each element is a complex Gaussian random variable with independent and identical distribution, satisfying E(|h i, j | 2 ) =1. Then the received signal can be expressed as:

y=Hs+n    (1)y=Hs+n (1)

即MIMO中一个发送符号(矢量s)含有多个发送信号(标量)。That is, one transmitted symbol (vector s) in MIMO contains multiple transmitted signals (scalars).

如果采用最大似然检测(ML)算法进行信号检测,最大似然解就是选取一组满足:If the maximum likelihood detection (ML) algorithm is used for signal detection, the maximum likelihood solution is to select a set of satisfy:

sthe s ^^ == argarg minmin ℵℵ || || ythe y -- HsHs || || 22 -- -- -- (( 22 ))

其中,为所有可能的发送信号的集合。当信号的调制阶数为m的时候,求出需要穷尽2m×N种可能排列。当天线数目较大、调制阶数较高的时候,ML算法的复杂度非常高,以至于无法应用,所以实际上一般采用次优的算法,例如,本发明所基于的球形译码(SphereDecoding,SD)的宽度优先算法(BFSD算法),业内常称为K-best SD算法。in, Set for all possible send signals. When the modulation order of the signal is m, find 2 m×N possible permutations need to be exhausted. When the number of antennas is large and the modulation order is high, the complexity of the ML algorithm is so high that it cannot be applied, so in fact, a suboptimal algorithm is generally used, for example, the Sphere Decoding (SphereDecoding, which is based on the present invention) SD) breadth-first algorithm (BFSD algorithm), often referred to as the K-best SD algorithm in the industry.

2、矩阵的三角化和树状搜索2. Triangulation and tree search of matrix

对矩阵H进行QR分解,H=QR,其中Q为N×N阶的酉矩阵,QHQ=E,E为单位矩阵,R是N×N阶的上三角矩阵,表示为:Perform QR decomposition on the matrix H, H=QR, where Q is a unitary matrix of N×N order, Q H Q=E, E is an identity matrix, and R is an upper triangular matrix of N×N order, expressed as:

则式(1)可以进一步表示为:Then formula (1) can be further expressed as:

ρ=Rs+η    (3)ρ=Rs+η (3)

其中:ρ=QHy,η=QHn。Among them: ρ=Q H y, η=Q H n.

或者,式(3)还可以表示为:Alternatively, formula (3) can also be expressed as:

进一步将式(3.a)展开,可以得到:Further expand formula (3.a), we can get:

ρρ ii == rr ii ,, ii sthe s ii ++ ΣΣ jj == ii ++ 11 NN (( rr ii ,, jj sthe s jj )) ++ ηη ii ,, ii == 1,21,2 ,, .. .. .. NN -- -- -- (( 44 ))

每一个计算出的ρi都可以看成是一层数据。从式(4)可以看到,第N层的接收数据ρN只由本层发送数据si决定,不受其它层的干扰;而第N-1层的接收数据ρN-1除了与第N-1层的发送数据sN-1有关外,还受到第N层发送数据sN的影响。一般来看,第i层的接收数据ρi不仅与第i层的发送数据si有关,还受第i+1层到第N层发送数据(si+1…sN)的影响,所以在已知第i+1层到第N层的发送数据的情况下,可以求得第i层的发送数据。Each calculated ρ i can be regarded as a layer of data. It can be seen from formula (4) that the received data ρ N of the Nth layer is only determined by the data s i sent by this layer and is not interfered by other layers; while the received data ρ N-1 of the Nth layer is not related to the Nth layer In addition to the data s N-1 sent by layer 1, it is also affected by the data s N sent by layer N. Generally speaking, the received data ρ i of the i-th layer is not only related to the sent data s i of the i-th layer, but also affected by the data sent from the i+1 layer to the N-th layer (s i+1 …s N ), so When the transmission data of the i+1th layer to the Nth layer are known, the transmission data of the i-th layer can be obtained.

设发送矢量的估计值为:Let the estimated value of the transmit vector be:

sthe s ^^ == sthe s ^^ 11 sthe s ^^ 22 ·&Center Dot; ·&Center Dot; ·&Center Dot; sthe s ^^ NN TT -- -- -- (( 55 ))

其中为第i层发送矢量的估计值,T表示转置。则截至第i层,已检测部分的发送矢量的估计值与接收值之间的欧氏距离(可以理解为经过信道的发送信号与接收信号的距离)为:in Send the estimate of the vector for the i-th layer, T for the transpose. Then up to the i-th layer, the Euclidean distance between the estimated value of the transmitted vector and the received value of the detected part (which can be understood as the distance between the transmitted signal and the received signal through the channel) is:

dd ii == ΣΣ jj == ii NN (( ρρ jj -- ΣΣ kk == jj NN rr jj ,, kk sthe s ^^ kk )) 22 -- -- -- (( 66 ))

因为第N层的接收数据ρN只与第N层发送数据sN有关,所以,信号检测可以先从第N层开始,顺序到第一层。如果考虑发送信号调制方式的所有取值,最开始检测的第N层有2m种选择,把每个取值的选择看作一个节点,每个节点都是一种可能的解。这样第N层就有2m个节点;其次检测的第N-1层相当于在第N层的基础上,将第N层的每个节点都展开计算2m个节点,因此共有2m×2m个节点;之后每层都在前层的节点数的基础上乘以2m,最后一层共有(2m)N=2N×m个节点。将这些节点按照它们被访问的次序连接起来,就组成2N×m条路径,这些路径从同一个节点出发、每个节点在下一层分出2m个节点,类似于一个倒立的树,如图4所示。SD系列算法的搜索过程就可用该图4来描绘,每一个节点都是一种可能的解。Because the received data ρ N of the Nth layer is only related to the sent data s N of the Nth layer, the signal detection can start from the Nth layer first and proceed to the first layer in sequence. If all the values of the modulation mode of the transmitted signal are considered, there are 2 m choices in the first Nth layer of detection, and each value choice is regarded as a node, and each node is a possible solution. In this way, there are 2 m nodes in the Nth layer; the second detected layer N-1 is equivalent to expanding each node in the Nth layer to calculate 2 m nodes on the basis of the Nth layer, so there are 2 m × 2 m nodes; after each layer, the number of nodes in the previous layer is multiplied by 2 m , and the last layer has (2 m ) N =2 N×m nodes. Connect these nodes according to the order they are visited to form 2 N×m paths, these paths start from the same node, and each node divides 2 m nodes in the next layer, similar to an inverted tree, such as Figure 4 shows. The search process of the SD series algorithm can be depicted in Figure 4, and each node is a possible solution.

设发送矢量的估计值为式(5),则树状搜索到第i层的时候的部分欧式距离就为(6),然后将本层内搜索的所有节点的部分欧式距离按照从小到大的次序排列,只保留最小的K个节点(或者可以理解为某个数目的节点)用于新的搜索。Assuming that the estimated value of the sending vector is formula (5), the partial Euclidean distance when the tree search reaches the i-th layer is (6), and then the partial Euclidean distance of all nodes searched in this layer is calculated according to the order from small to large Arranged in order, only keep the smallest K nodes (or can be understood as a certain number of nodes) for new searches.

新一层的计算则是在前一层保留节点的基础上展开新的取值选择,利用这些取值就可以求出部分发送矢量与部分接收矢量之间的部分欧式距离。将这些部分欧式距离按从小到大或者从大到小的顺序排序,保留部分欧式距离小于阈值的固定数目的节点,则该新的一层检测结束。The calculation of the new layer is to expand the new value selection on the basis of the reserved nodes of the previous layer, and use these values to calculate the partial Euclidean distance between the partial sending vector and the partial receiving vector. These partial Euclidean distances are sorted from small to large or from large to small, and a fixed number of nodes with partial Euclidean distances smaller than the threshold are reserved, and the new layer of detection ends.

在本发明的一个例子中,图3给出一个四层最大似然搜索的示意图。In an example of the present invention, FIG. 3 shows a schematic diagram of a four-level maximum likelihood search.

图4示出了一个本发明的树状搜索示意图,其中假设每层最多保留8个节点,每个节点最多可能的子节点数目为4。8个节点按照从小到大的次序从左到右排列,它们被分为4组,每组有两个节点,第一组里两个节点的子节点的数目是4,第二、三、四组的子节点的数目分别是3、2、1。图4中,实线的直线对应的圆圈表示展开的节点,虚线的直线对应的圆圈表示可能的但是没有展开的节点。实线的圆圈表示保留的节点,虚线的圆圈表示不保留的节点。Figure 4 shows a schematic diagram of a tree search of the present invention, in which it is assumed that each layer retains up to 8 nodes, and the maximum number of possible child nodes for each node is 4. The 8 nodes are arranged from left to right in order from small to large , they are divided into 4 groups, each group has two nodes, the number of child nodes of the two nodes in the first group is 4, and the number of child nodes of the second, third, and fourth groups are 3, 2, and 1, respectively. In FIG. 4 , the circles corresponding to the solid straight lines represent expanded nodes, and the circles corresponding to the dashed straight lines represent possible but not expanded nodes. Circles with solid lines indicate nodes that are kept, and circles with dashed lines indicate nodes that are not kept.

图5、图6示出了根据本发明的一个实施例的仿真实验的结果。其中实验环境为8发送天线、8接收天线、16-/64-/256-QAM调制方式(分别用m=4、m=8、m=16表示)。图5和图6中有三种算法的性能比较,传统的K-best SD算法(图中用KSD表示)、本发明的算法(图中用SRKSD表示)、以及固定复杂度的球形译码算法(图中用FSD表示)。图5表示在没有排序情况下的性能曲线,图6表示在排序情况下的性能曲线,所有算法前都加“O”以示排序。图的横轴表示信噪比SNR、纵轴表示误比特率BER性能。其中传统的K-best SD算法的K取值与本发明算法一样,所以计算复杂度大于本算法,固定复杂度的球形译码的计算复杂度与本算法一样。从图5、图6均可看出,本发明的方法性能与K-best SD算法性能基本一样,从图中无法分辨性能的差别。图5可以看出在没有排序的情况下,本发明的方法的性能要大大优于FSD算法,在BER=10-3的情况下,本发明的方法在三种调制方式下的性能要优于FSD算法大于5dB。从图6可以看出在有排序的情况下,本发明的方法的性能要优于FSD算法。在BER=10-3,调制方式取16-QAM的时候,本发明的方法比同样复杂度的FSD算法好0.5dB左右。Fig. 5 and Fig. 6 show the results of simulation experiments according to an embodiment of the present invention. The experimental environment is 8 transmitting antennas, 8 receiving antennas, and 16-/64-/256-QAM modulation mode (represented by m=4, m=8, and m=16 respectively). The performance comparison of three kinds of algorithms is arranged among Fig. 5 and Fig. 6, traditional K-best SD algorithm (represented by KSD among the figure), the algorithm of the present invention (represented by SRKSD among the figure), and the spherical decoding algorithm of fixed complexity (represented by KSD among the figure) It is represented by FSD in the figure). Figure 5 shows the performance curve without sorting, and Figure 6 shows the performance curve in the sorting case, and all algorithms are preceded by "O" to show sorting. The horizontal axis of the graph represents the signal-to-noise ratio SNR, and the vertical axis represents the bit error rate BER performance. Wherein the value of K of the traditional K-best SD algorithm is the same as the algorithm of the present invention, so the computational complexity is greater than the present algorithm, and the computational complexity of the fixed-complexity sphere decoding is the same as the present algorithm. It can be seen from Fig. 5 and Fig. 6 that the performance of the method of the present invention is basically the same as that of the K-best SD algorithm, and the performance difference cannot be distinguished from the figures. It can be seen from Fig. 5 that without sorting, the performance of the method of the present invention is much better than that of the FSD algorithm, and in the case of BER=10 -3 , the performance of the method of the present invention is better than that of the three modulation modes FSD algorithm is greater than 5dB. It can be seen from Fig. 6 that in the case of sorting, the performance of the method of the present invention is better than that of the FSD algorithm. When BER=10 -3 and the modulation mode is 16-QAM, the method of the present invention is about 0.5dB better than the FSD algorithm with the same complexity.

从而可以看出,本发明的实施例实现了与在宽度一样的情况下与宽度优先球形译码算法的性能基本一样,在计算复杂度一样的情况下,性能优于固定复杂度的球形译码算法。Thus it can be seen that the embodiment of the present invention achieves the same performance as the breadth-first sphere decoding algorithm in the case of the same width, and in the case of the same computational complexity, the performance is better than that of the fixed-complexity sphere decoding algorithm. algorithm.

本发明还提供了一种MIMO系统的信号检测装置,包括:The present invention also provides a signal detection device for a MIMO system, including:

信号接收单元,用于接收符号;a signal receiving unit, configured to receive symbols;

信号检测单元,用于首先利用已知的信道矩阵和可能的发送符号处理所述信号接收单元接收到的符号以构成搜索树,并按照以下方式对所述搜索树的第N层进行搜索:The signal detection unit is configured to first process the symbols received by the signal receiving unit by using the known channel matrix and possible transmission symbols to form a search tree, and search the Nth layer of the search tree in the following manner:

将当前保留节点分为M组,每个节点对应一个发送符号的一部分元素组成的部分发送矢量,所述发送符号号表示为发送矢量的形式;Dividing the currently reserved nodes into M groups, each node corresponds to a partial transmission vector composed of a part of elements of a transmission symbol, and the transmission symbol number is expressed in the form of a transmission vector;

按照每组内节点的展开子节点数相等、不同组内节点的展开子节点数从M开始依次递减的原则,展开第N层节点的子节点,展开子节点的方式为在所述节点所对应的部分发送矢量后面添加新的元素,直到形成整个发送矢量,所述子节点对应所述新的元素,N为所述搜索树的总层数;According to the principle that the number of expanded child nodes of nodes in each group is equal, and the number of expanded child nodes of nodes in different groups decreases sequentially from M, the child nodes of the Nth layer node are expanded. Add new elements behind the part of the sending vector until the entire sending vector is formed, the child nodes correspond to the new elements, and N is the total number of layers of the search tree;

其次按照所有展开的子节点对应的部分发送矢量与部分接收矢量之间的部分欧氏距离进行排序,保留部分欧氏距离最小的K个子节点用于下一层的搜索,所述部分接收矢量由与所述发送符号对应的接收符号的一部分元素组成,所述接收符号表示为接收矢量的形式,N、K、M均为正整数;Secondly, sort according to the partial Euclidean distance between the partial sending vector and the partial receiving vector corresponding to all expanded sub-nodes, keep the K sub-nodes with the smallest partial Euclidean distance for the search of the next layer, and the partial receiving vector is determined by Composed of a part of elements of a received symbol corresponding to the sent symbol, the received symbol is expressed in the form of a received vector, and N, K, and M are all positive integers;

再次搜索所述搜索树的其它层,在由子节点组成的路径中寻找欧氏距离最小的路径作为搜索结果;Search the other layers of the search tree again, and find the path with the smallest Euclidean distance in the path composed of child nodes as the search result;

信号输出单元,用于将所述信号检测单元得到的搜索结果输出。A signal output unit, configured to output the search result obtained by the signal detection unit.

由以上实施例可以看出,本发明通过对搜索树每层搜索时展开节点数进行限制的方法来减少搜索的节点数,在基本不降低检测性能的基础上,降低了信号检测的复杂度,提高了接收数据的检测效率,而且其硬件实现电路更加简单,电路面积更小、功耗更低。本发明的方法还可应用于多用户信号检测及其它信号检测。It can be seen from the above embodiments that the present invention reduces the number of nodes to be searched by limiting the number of nodes to be expanded when searching for each layer of the search tree, and reduces the complexity of signal detection without substantially reducing the detection performance. The detection efficiency of the received data is improved, and its hardware implementation circuit is simpler, the circuit area is smaller, and the power consumption is lower. The method of the present invention can also be applied to multi-user signal detection and other signal detection.

以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明技术原理的前提下,还可以做出若干改进和替换,这些改进和替换也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and replacements can also be made, these improvements and replacements It should also be regarded as the protection scope of the present invention.

Claims (10)

1. a signal detecting method for mimo system, is characterized in that, comprises the following steps:
S1, utilize known channel matrix and possible transmission symbol construction search tree;
S2, according to step S21~S23, the N layer of described search tree is searched for total number of plies that N is described search tree:
S21, current reservation node is divided into M group, the part that the corresponding a part of element that sends symbol of each node forms sends vector, and described transmission symbol table is shown the form that sends vector;
S22, according to the expansion son node number of every group of interior nodes equates, the expansion son node number of interior nodes does not start to successively decrease successively from M on the same group principle, launch the child node of N node layer, launch the mode of child node for add new element after the corresponding part transmission of described node vector, until form whole transmission vector, the corresponding described new element of described child node;
S23, the part of Euclidean distance receiving between vector according to part transmission vector corresponding to the child node of all expansion and part sort, K child node of reserve part Euclidean distance minimum is for the search of lower one deck, described part reception vector is comprised of a part of element of the receiving symbol corresponding with described transmission symbol, described receiving symbol is expressed as the form that receives vector, and N, K, M are positive integer;
S3, according to the mode of step S21~S23 search for described search tree other layer, in the path being formed by child node, find the path of Euclidean distance minimum as Search Results.
2. the method for claim 1, is characterized in that, step S1 is specially: by each line ordering of advancing of described channel matrix, then utilize described possible transmission symbol trigonometric ratio to form described search tree the channel matrix obtaining after sequence.
3. the method for claim 1, is characterized in that, the described possible transmission symbol choosing set that freely all permutation and combination of each antenna possibility transmitted signal form.
4. method as claimed in claim 2, is characterized in that, described by the mode of channel matrix trigonometric ratio for channel matrix is carried out to QR decomposition.
5. method as claimed in claim 2, it is characterized in that, in step S1, by the mode of each line ordering of advancing of described channel matrix, be: if all nodes of this row are less than or equal to K, according to gain order from small to large, sort, otherwise sort according to gain order from big to small.
6. the method for claim 1, is characterized in that, the initial value of M is for sending the number of symbol, and the value of M reduces along with increasing of the number of plies of search.
7. the method for claim 1, is characterized in that, the mode that forms path by child node in step S3 is: from every one deck, select a child node to be connected in turn and form 2 n * mpaths, and the child node of selected j layer is all to launch from the selected child node of last layer, j=2 ..., N, m is for sending the order of modulation of symbol.
8. the method for claim 1, it is characterized in that, step by K child node of reserve part Euclidean distance minimum in step S23 for the search of lower one deck replaces with: K child node of reserve part Euclidean distance minimum, and delete the child node that Euclidean distance value is greater than distance threshold from a described K child node, remaining child node is for the search of lower one deck.
9. method as claimed in claim 7, is characterized in that, the number of K reduces along with increasing of the number of plies of search.
10. a signal supervisory instrument for mimo system, is characterized in that, comprising:
Signal receiving unit, for receiving symbol;
Detecting signal unit, for first utilizing known channel matrix and possible transmission symbol to process symbol that described signal receiving unit receives to form search tree, and in such a way the N layer of described search tree is searched for:
Current reservation node is divided into M group, and the corresponding part that sends a part of element composition of symbol of each node sends vector, and described transmission symbol table is shown the form that sends vector;
According to the principle that the expansion son node number of every group of interior nodes equates, the expansion son node number of interior nodes does not start to successively decrease successively from M on the same group, launch the child node of N node layer, launch the mode of child node for add new element after the corresponding part transmission of described node vector, until form whole transmission vector, the corresponding described new element of described child node, total number of plies that N is described search tree;
Secondly the part of Euclidean distance that sends vector according to part corresponding to the child node of all expansion and partly receive between vector sorts, K child node of reserve part Euclidean distance minimum is for the search of lower one deck, described part reception vector is comprised of a part of element of the receiving symbol corresponding with described transmission symbol, described receiving symbol is expressed as the form that receives vector, and N, K, M are positive integer;
Again search for other layer of described search tree, in the path being formed by child node, find the path of Euclidean distance minimum as Search Results;
Signal output unit, for the Search Results output that described detecting signal unit is obtained.
CN201210191954.6A 2012-06-11 2012-06-11 Signal detection method and device of MIMO (multiple input multiple output) system Expired - Fee Related CN102723975B (en)

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