WO2011153802A1 - 一种多输入多输出系统的信号检测方法和装置 - Google Patents

一种多输入多输出系统的信号检测方法和装置 Download PDF

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WO2011153802A1
WO2011153802A1 PCT/CN2010/079832 CN2010079832W WO2011153802A1 WO 2011153802 A1 WO2011153802 A1 WO 2011153802A1 CN 2010079832 W CN2010079832 W CN 2010079832W WO 2011153802 A1 WO2011153802 A1 WO 2011153802A1
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signal
detection
noise ratio
algorithm
radius
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French (fr)
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朱登魁
鲁照华
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中兴通讯股份有限公司
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00Arrangements for detecting or preventing errors in the information received
    • H04L1/004Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045Arrangements at the receiver end
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00Arrangements for detecting or preventing errors in the information received
    • H04L1/02Arrangements for detecting or preventing errors in the information received by diversity reception
    • H04L1/06Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity

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  • the present invention relates to signal detection techniques for wireless communication systems, and more particularly to a signal detection method and apparatus for a multiple input multiple output (MIMO) system.
  • MIMO multiple input multiple output
  • MIMO technology has become one of the key technologies of the new generation of wireless communication systems.
  • the MIMO system uses multiple antennas at both the transmitting and receiving ends, and multiple data streams are transmitted and received at the same time and frequency band.
  • SISO single-input single-output
  • the receiving end of the MIMO system receives multiple signals overlapping in time and frequency band, so the signal detection complexity of the MIMO system is much higher than that of the SISO system. Signal Detection.
  • the signal detection of MIMO systems can use the Maximum Likelihood Detection (ML) method, but the maximum likelihood detection needs to traverse all possible transmission vectors.
  • the complexity is exponentially related to the product of the modulation order and the number of transmitting antennas. In the case where the number and the number of transmitting antennas are large, since the complexity is too high, it is basically impossible to use in an actual system.
  • many researchers have proposed some improved algorithms, including a sphere decoding algorithm, a K-Best algorithm, etc., and the K-Best algorithm is the K most reliable path. Detection algorithm.
  • the sphere decoding algorithm is a depth-first based tree search algorithm.
  • the basic idea is to search all nodes in a hypersphere with a radius d around the received signal y to reduce computational complexity.
  • the complexity of spherical decoding is exponential with the initial spherical radius.
  • Reasonable initial spherical radius selection is important for reducing the complexity of spherical decoding. Excessive initial spherical radius can lead to excessive computational complexity. A small initial spherical radius may in turn cause the search to fail.
  • the K-Best algorithm is also a tree search based MIMO detection algorithm, the difference is that K-Best
  • the algorithm is a tree search algorithm based on width first. In the search of each layer, the K-Best algorithm keeps only K nodes, and continues to search among the K nodes.
  • the K-Best algorithm is also known as the M algorithm.
  • the MIMO system model is shown in FIG. 1.
  • the number of transmitting antennas is M
  • the number of receiving antennas is N, which is expressed by the formula (1):
  • y Hs + n ( 1 )
  • [ ⁇ 2 ,..., ⁇ denotes the Wxl-dimensional received signal vector
  • 8 [ ⁇ 2 ,...,3 ⁇ 4 denotes the MXI-dimensional transmit signal vector,! !
  • H is the NxM-dimensional channel gain matrix.
  • 0 [( ⁇ , 0 2 ] is the unitary matrix of the dimension, (the dimension of ⁇ is NxM, the dimension of Q 2 is Nx(N-M), R is the upper triangular matrix of MxM dimension, O is ( N-M) The zero matrix of the xM dimension.
  • the initial search radius of the sphere decoding can be calculated by the noise variance, as shown in the following equation (7):
  • is a gamma function and is an integral variable.
  • the K-Best algorithm is based on breadth-first search. A certain number of nodes are selected in each layer, and the number of nodes in each layer can be different, and then path expansion is performed. For example, in the M layer, selected such that a minimum 3 ⁇ 4A I 2, may be generated by these 3 ⁇ 4 ⁇ . ⁇ node, [Omega] is the size of the constellation, in ⁇ . ⁇ node, select such a
  • the sphere decoding algorithm can obtain the same performance as the maximum likelihood detection method, but the complexity of the sphere decoding algorithm is greatly affected by the signal-to-noise ratio. In the case of low signal-to-noise ratio, the complexity of sphere decoding will be Very high.
  • the K-Best algorithm is a suboptimal method with respect to the maximum likelihood detection method. Its complexity is only related to the number of nodes retained in each layer, and is not affected by the signal-to-noise ratio.
  • the main object of the present invention is to provide a signal detection method and apparatus for a multiple input multiple output system, which can realize signal detection of a MIMO system by using a suitable detection algorithm under different signal to noise ratios. .
  • the invention provides a signal detection method for a multiple input multiple output system, which sets a signal to noise ratio threshold; the method further comprises:
  • the ball decoding algorithm is used for signal detection; when the signal-to-noise ratio is not greater than the signal-to-noise ratio threshold, K- The Best algorithm performs signal detection.
  • the set signal to noise ratio threshold is: for each channel in each signal to noise ratio condition
  • the number of operations of the sphere decoding algorithm and the K-Best algorithm is statistically obtained, and the signal-to-noise ratio when the number of operations of the sphere decoding algorithm exceeds and is closest to the number of operations of the K-Best algorithm is taken as the signal-to-noise ratio threshold.
  • the signal detection by using the sphere decoding algorithm is: setting an initial search radius and a maximum search radius; performing signal detection of the sphere decoding algorithm according to the detection radius, and ending the process when the detection is successful; When successful, increase the detection radius and determine whether the increased radius is greater than the maximum search radius. When it is greater than, use the K-Best algorithm to detect the signal; when it is not greater than, the sphere is increased according to the detected radius. Signal detection by the decoding algorithm.
  • the signal detection by using the sphere decoding algorithm is as follows: ⁇ a suboptimal solution is obtained by using a algorithm with a relative maximum likelihood detection suboptimal, and the distance between the suboptimal solution and the received signal vector is used as an initial search radius. Perform signal detection of the sphere decoding algorithm.
  • the method when setting the initial search radius and the maximum search radius, the method further includes: setting a maximum number of search nodes, performing a search for the visited node when performing signal detection of the sphere decoding algorithm according to the detection radius Judging by the number, when the number of nodes visited by the search reaches the set maximum number of search nodes, the grid point closest to the received signal vector is output as the detection result; when the number of nodes visited by the search reaches the set maximum number of search nodes, When the grid point is still not found, the K-Best algorithm is used for signal detection.
  • the present invention provides a signal detecting apparatus for a multiple input multiple output system, the apparatus comprising: a signal to noise ratio threshold module, a signal to noise ratio module, a comparison module, a K-Best algorithm module, and a sphere decoding algorithm module;
  • a signal to noise ratio threshold module for setting a signal to noise ratio threshold
  • a signal to noise ratio module configured to acquire a signal to noise ratio of a current channel
  • the comparison module is configured to notify the spherical decoding algorithm module when the signal to noise ratio is greater than the signal to noise ratio threshold; and notify the K-Best algorithm module when the signal to noise ratio is not greater than the signal to noise ratio threshold;
  • the K-Best algorithm module for detecting signals by K-Best algorithm;
  • the sphere decoding algorithm module is used for signal detection by using a sphere decoding algorithm.
  • the spherical decoding algorithm module is specifically configured to set an initial search radius and a maximum search radius; perform signal detection of the sphere decoding algorithm according to the detection radius, and end the operation when the detection is successful; when the detection is unsuccessful Increase the detection radius and determine whether the increased radius is greater than the maximum search radius.
  • the K-Best algorithm module is notified; otherwise, the signal detection of the sphere decoding algorithm is performed according to the increased detection radius.
  • the spherical decoding algorithm module is further configured to obtain a suboptimal solution by using a algorithm with a relative maximum likelihood detection suboptimal, and use the distance between the suboptimal solution and the received signal vector as an initial search radius.
  • the spherical decoding algorithm module outputs a maximum likelihood solution as a detection result when the detection is successful; or, in addition to outputting the maximum likelihood solution, outputs a solution that is closest to the maximum likelihood solution, and obtains a transmission bit. Soft information.
  • the spherical decoding algorithm module is further configured to set a maximum number of search nodes; when performing signal detection of the sphere decoding algorithm according to the detection radius, performing the judgment of the number of nodes visited by the search, when the search is accessed When the number of nodes reaches the set maximum number of search nodes, the grid point closest to the received signal vector is output as the detection result; when the number of nodes visited by the search reaches the set maximum number of search nodes, and the grid point is still not found , notify the K-Best algorithm module.
  • the invention provides a signal detection method and device for a MIMO system, which sets a signal to noise ratio threshold; acquires a signal to noise ratio of a current channel, and uses a sphere decoding algorithm when a signal to noise ratio is greater than the signal to noise ratio threshold Perform signal detection; when the signal-to-noise ratio is not greater than the signal-to-noise ratio threshold, use the K-Best algorithm to detect the signal; thus, it is possible to use appropriate detection in different signal-to-noise ratios.
  • the algorithm implements signal detection of the MIMO system.
  • Figure 1 is a schematic diagram of a MIMO system model
  • FIG. 2 is a schematic diagram of a flow of a signal detection method for implementing a MIMO system according to the present invention
  • 3 is a schematic diagram of a structure of a signal detecting apparatus for implementing a MIM0 system according to the present invention
  • FIG. 4 is a schematic diagram of a signal detecting method for implementing a MIM0 system according to Embodiment 1
  • FIG. 5 is a schematic diagram of implementing a signal of a MIMO system according to Embodiment 2 Schematic diagram of the process flow of the test.
  • the basic idea of the present invention is: setting a signal to noise ratio threshold; acquiring a signal to noise ratio of a current channel, and when the signal to noise ratio is greater than the signal to noise ratio threshold, using a sphere decoding algorithm for signal detection; when the signal to noise ratio is not When the signal to noise ratio is greater than the threshold, the K-Best algorithm is used for signal detection.
  • the present invention implements a signal detection method for a MIMO system. As shown in FIG. 2, the method includes the following steps:
  • Step 201 setting a signal to noise ratio threshold
  • the number of operations of the sphere decoding algorithm and the K-Best algorithm is statistically obtained under the condition of each signal to noise ratio of the current channel, and the number of operations of the sphere decoding algorithm exceeds and is closest to the number of operations of the K-Best algorithm.
  • Signal to noise ratio as a signal to noise ratio threshold;
  • the signal to noise ratio threshold can be adaptively adjusted according to the condition of the channel during the detection process.
  • Step 202 Obtain a signal-to-noise ratio of the current channel, determine whether the signal-to-noise ratio is greater than a signal-to-noise ratio threshold, if not greater, perform step 206; if greater, perform step 203;
  • Step 203 Set an initial search radius and a maximum search radius of the sphere decoding algorithm.
  • the initial search radius may be obtained by the formula (7); or a suboptimal solution ⁇ may be obtained by using a algorithm with a relative maximum likelihood detection suboptimal, and the distance between the suboptimal solution and the received signal vector y is obtained.
  • the initial search radius as shown below:
  • the algorithm for the relative maximum likelihood detection suboptimal includes: K-Best algorithm, zero-forcing algorithm, minimum mean square error algorithm, etc.;
  • the maximum search radius can also be obtained by:
  • n is twice the number of s ⁇ radio antennas
  • ⁇ 2 is the noise variance, and satisfies ⁇ ".
  • the step further includes setting a maximum number of search nodes.
  • Step 204 Perform signal detection of the sphere decoding algorithm according to the detection radius. When the detection is successful, the process ends; when the detection is unsuccessful, step 205 is performed;
  • the detection radius is the set initial search radius.
  • the detection result output by the sphere decoding algorithm may adopt a hard decision method, that is, output a maximum likelihood solution as a detection result; or a soft output method may be used, that is, the output is maximum
  • a hard decision method that is, output a maximum likelihood solution as a detection result
  • a soft output method may be used, that is, the output is maximum
  • several solutions closest to the maximum likelihood solution are also output, and the soft information of the transmitted bits is calculated using these solutions.
  • Step 203 when the signal detection of the sphere decoding algorithm is performed according to the detection radius, the number of nodes visited by the search is determined, and the number of nodes visited by the search is reached.
  • the maximum number of search nodes is set, the grid point closest to the received signal vector is output as the detection result, and the flow is ended; when the number of nodes visited by the search reaches the set maximum number of search nodes, and the lattice point is still not obtained, the execution is performed.
  • Step 206 Step 205: increase the detection radius, and determine whether the increased radius is greater than the maximum search radius, when greater than, step 206; otherwise, perform step 204;
  • the method for increasing the detected radius may be a linear increase between the initial search radius and the maximum search radius, or may be increased by a half-fold method, that is, the increased radius takes the current radius.
  • Step 206 Perform signal detection by using the K-Best algorithm, and end the process
  • This step further includes: Since the detection result output by the K-Best algorithm is not necessarily the maximum likelihood solution, a solution near the maximum likelihood solution is usually obtained, and in order to further improve the performance of the K-Best algorithm detection, the soft output may be used.
  • the method that is, when the K-Best algorithm is used to detect the arrival of the first layer, the optimal K solutions are output for the calculation of the bit soft information, and the K is a natural number.
  • the present invention further provides a signal detecting apparatus for a MIMO system.
  • the apparatus includes: a signal to noise ratio threshold module 31, a signal to noise ratio module 32, a comparison module 33, and a K-Best.
  • a signal to noise ratio threshold module 31 for setting a signal to noise ratio threshold
  • the signal-to-noise ratio threshold module 31 statistically obtains the number of operations of the sphere decoding algorithm and the K-Best algorithm on the current channel under various signal-to-noise ratio conditions, and the number of operations of the sphere decoding algorithm exceeds and is closest to The signal-to-noise ratio of the K-Best algorithm is used as the signal-to-noise ratio threshold;
  • a signal to noise ratio module 32 configured to acquire a signal to noise ratio of a current channel
  • the comparison module 33 is configured to notify the spherical decoding algorithm module 35 when the signal to noise ratio is greater than the signal to noise ratio threshold; and notify the K-Best algorithm module 34 when the signal to noise ratio is not greater than the signal to noise ratio threshold;
  • the K-Best algorithm module 34 is configured to perform signal detection by using a K-Best algorithm
  • the K-Best algorithm module 34 is further configured to output an optimal K solutions for performing bit soft information calculation when the K-Best algorithm detects the arrival of the first layer, where the K is a natural number.
  • the sphere decoding algorithm module 35 is configured to perform signal detection by using a sphere decoding algorithm. Specifically, the sphere decoding algorithm module 35 sets an initial search radius and a maximum search half. The signal is detected by the sphere decoding algorithm according to the detection radius. When the detection is successful, the operation ends; when the detection is unsuccessful, the detection radius is increased, and it is determined whether the increased radius is greater than the maximum search radius. Notifying the K-Best algorithm module 34; otherwise, performing signal detection of the sphere decoding algorithm according to the increased detection radius. Wherein, when the signal detection of the sphere decoding algorithm is performed for the first time, the detection radius is an initial search radius.
  • the algorithm that uses the relative maximum likelihood detection suboptimal algorithm obtains a suboptimal solution, and the distance between the suboptimal solution and the received signal vector is used as an initial search.
  • the sphere decoding algorithm module 35 does not need to set the maximum search radius;
  • the spherical decoding algorithm module 35 may output a detection result using a hard decision method, that is, output a maximum likelihood solution as a detection result; or use a soft output method, that is, in addition to the output. In addition to the maximum likelihood solution, several solutions closest to the maximum likelihood solution are also output, and the soft information of the transmitted bits is obtained by using these solutions;
  • the sphere decoding algorithm module 35 is further configured to set a maximum number of search nodes; when performing signal detection of the sphere decoding algorithm according to the detection radius, the number of nodes visited by the search is determined, and when the search is accessed When the number of nodes reaches the set maximum number of search nodes, the grid point closest to the received signal vector is output as the detection result, and the operation ends; when the number of nodes visited by the search reaches the set maximum number of search nodes, and the number of search nodes is still not obtained.
  • the K-Best algorithm module 34 is notified.
  • Embodiment 1 The distance between the suboptimal solution and the received signal vector y is used as the initial search radius of the sphere decoding.
  • the present invention implements a signal detection method for a MIMO system. As shown in FIG. 4, the method includes the following steps. :
  • Step 401 setting a signal to noise ratio threshold
  • the spherical decoding algorithm is obtained by statistically calculating the current channel under various SNR conditions.
  • the number of operations of the K-Best algorithm is as the signal-to-noise ratio threshold when the number of operations of the spherical decoding algorithm exceeds and is closest to the number of operations of the K-Best algorithm;
  • Step 402 Obtain a signal-to-noise ratio of the current channel, determine whether the signal-to-noise ratio is greater than a signal-to-noise ratio threshold, if not greater, perform step 405; if greater, perform step 403;
  • Step 403 Set an initial search radius of the sphere decoding algorithm.
  • the algorithm that uses the relative maximum likelihood detection suboptimal algorithm obtains a suboptimal solution.
  • the distance between the suboptimal solution and the received signal vector y is taken as the initial search radius, as shown in the following formula:
  • the algorithm for the relative maximum likelihood detection suboptimal includes: a K-Best algorithm, a zero forcing algorithm, a minimum mean square error algorithm, and the like.
  • Step 404 Perform detection of a received signal of a sphere decoding algorithm according to an initial search radius, and end the process;
  • the detection result output by the sphere decoding algorithm may adopt a hard decision method, that is, output a maximum likelihood solution as a detection result; or a soft output method, that is, in addition to outputting a maximum likelihood solution, Outputs a number of solutions that are closest to the maximum likelihood solution, and use these solutions to calculate the soft information of the transmitted bits.
  • Step 405 The K-Best algorithm is used to detect the received signal, and the process ends.
  • the step further includes: Since the detection result output by the K-Best algorithm is not necessarily the maximum likelihood solution, generally obtained near the maximum likelihood solution Solution, in order to further improve the performance of the K-Best algorithm detection, the soft output method can be used, that is, when the K-Best algorithm is used to detect the arrival of the first layer, the optimal K solutions are output for the calculation of the bit soft information.
  • the K is a natural number.
  • Embodiment 2 Setting a maximum number of search nodes in a sphere decoding algorithm, the present invention implements a signal detection method for a MIMO system. As shown in FIG. 5, the method includes the following steps: Step 501: Setting a signal to noise ratio Wide value
  • the spherical decoding algorithm is obtained by statistically calculating the current channel under various SNR conditions.
  • the number of operations of the K-Best algorithm is the signal-to-noise ratio threshold when the number of operations of the sphere decoding algorithm exceeds and is closest to the number of operations of the K-Best algorithm.
  • Step 502 Obtain a signal-to-noise ratio of the current channel, determine whether the signal-to-noise ratio is greater than a signal-to-noise ratio threshold, if not greater, perform step 507; if greater, perform step 503;
  • Step 503 Set an initial search radius of the sphere decoding algorithm and a maximum number of search nodes.
  • Step 504 Perform detection of a received signal of a sphere decoding algorithm according to the set initial search radius and the maximum number of search nodes;
  • Step 505 When the number of nodes visited by the search reaches the set maximum number of search nodes, it is determined whether a grid point is obtained; if yes, step 506 is performed; otherwise, step 507 is performed;
  • Step 506 Output a grid point closest to the received signal vector as a detection result, and end the flow;
  • Step 507 Detect the received signal by using the K-Best algorithm, and end the flow;
  • This step further includes: detecting by the K-Best algorithm output
  • the result is not necessarily the maximum likelihood solution. Usually, the solution near the maximum likelihood solution is obtained.
  • the soft output method can be used, that is, the K-Best algorithm is used to detect the arrival.
  • the optimal K solutions are output for the calculation of the bit soft information, and the K is a natural number.

Abstract

本发明公开了一种多输入多输出系统的信号检测方法,设定信噪比阈值;获取当前信道的信噪比,当信噪比大于所述信噪比阈值时,采用球形译码算法进行信号的检测;当信噪比不大于所述信噪比阈值时,采用K-Best算法进行信号的检测;本发明同时公开了一种多输入多输出系统的信号检测装置,通过本发明的方案,可以实现在不同的信噪比的情况下,采用合适的检测算法来实现多输入多输出系统的信号检测。

Description

一种多输入多输出系统的信号检测方法和装置 技术领域
本发明涉及无线通信系统的信号检测技术, 尤其涉及一种多输入多输 出 (MIMO ) 系统的信号检测方法和装置。 背景技术
MIMO技术已经成为新一代无线通信系统的关键技术之一。 MIMO 系 统在发射端和接收端均釆用多个天线, 多个数据流在相同时间和频带被发 送和接收。 与传统的单输入单输出 (SISO ) 系统相比, MIMO 系统接收端 接收到的是在时间上和频带上均相互重叠的多路信号, 因此 MIMO系统的 信号检测复杂度远高于 SISO系统的信号检测。
MIMO系统的信号检测可以釆用最大似然检测(ML )方法, 但最大似 然检测需要遍历所有可能的发射向量, 其复杂度与调制阶数和发射天线数 的乘积成指数关系, 在调制阶数和发射天线数较大的情况下, 由于其复杂 度太高, 在实际系统中基本无法釆用。 为了在保持最大似然算法的性能的 同时降低计算复杂度, 很多学者提出了一些改进的算法, 其中包括球形译 码算法、 K-Best算法等, 所述 K-Best算法为 K个最可靠路径检测算法。
球形译码算法是一种基于深度优先的树搜索算法, 基本思想是, 只在 接收到的信号 y周围半径为 d的超球内对所有节点进行搜索, 以此来减小 计算复杂度。
球形译码的复杂度与初始球半径呈指数式关系, 合理的初始球半径选 取对于降低球形译码的复杂度具有重要意义, 过大的初始球半径会导致过 大的运算复杂度, 而过小的初始球半径又可能导致搜索失败。
K-Best算法也是一种基于树搜索的 MIMO检测算法,不同的是, K-Best 算法是一种基于宽度优先的树搜索算法。 K-Best算法在每一层的搜索中, 只保留 K个节点, 在这 K个节点中继续搜索。 在有些文献中, K-Best算法 也称为 M算法。
现有技术中, MIMO系统模型如图 1所示, 发射天线数为 M, 接收天 线数为 N, 用公式(1 )表示为:
y = Hs + n ( 1 ) 其中, Υ = [υ2,...,^νΓ表示 Wxl维接收信号向量, 8 = [^2,...,¾ 表 示 MXI维发送信号向量,!!二^,^,…, 表示 NXl维接收端噪声向量, H 为 NxM维的信道增益矩阵。
釆用最大似然方法可以得到公式(2):
SM gminlly- seQ
对 H作 QR分解得到式(3):
Figure imgf000004_0001
其中, 0 = [(^,02]为 维的酉矩阵, (^的维数为 NxM, Q2的维 数为 Nx(N- M), R为 MxM维的上三角矩阵, O为(N- M)xM维的零 矩阵。
由于 Q为酉矩阵, 可以得到式(4): y-H-s||2= Qf -y-R-s 2+ Q -y2 (4) 定义 y' = Q .y, C= Qf y , 由式(4)得到式(5):
Figure imgf000005_0001
M M
∑\y:- 球形译码算法只在接收信号 y周围半径为 d的超球内对所有节点进行 搜索, 得到相应格点, 即 ||y- H.s||2≤62 , 代入(5) 式, ^ d,2=d2-C, 可以得到式(6):
Figure imgf000005_0002
由 I ; - ¾, ½ l2≤ d'1可以求得节点 Sm的可能取值 , 把所有可能的节点
½代入 I i - rM sM I2 + 1 yM_x - rM_l sM - rM_l _xsM_x |2< d'1 , 可以求得节点 的可能取值, 以此类推, 一直到达第 1层, 得到节点 的可能取值; 上 述搜索到的节点序列 〜 的组合作为相应的球形译码获得的格点。
球形译码的初始搜索半径 可以通过噪声方差来计算, 如下式(7) 所 示:
Figure imgf000005_0003
其中"为初始搜索半径系数, η为两倍发射天线数, σ2为噪声方差。 在这个球内至少能找到一个星座点的概率为:
Figure imgf000005_0004
其中, Γ为伽玛函数, 为积分变量。
例如, 当" = 1.0, « = 8时, 概率 1— £·为 0.5665; 当" = 2.0, « = 8时, 概率 1— £·为 0.9576。 K-Best算法基于宽度优先的搜索, 在每层选择一定数目的节点, 每层 节点的数目可以不同, 然后再进行路径扩展。 例如, 在第 M层, 选择使得 ¾A I2最小的 个 , 由这些 ¾可以生成 Μ . Ω个节点, Ω为星 座 的 大 小 , 在 Μ . Ω 个 节 点 中 , 选 择 个 使 得
WM ~ RM,MSM t + \ yM-l - RM-l,MSM ~ ¾-l,M-l½-l f最小的 ^,以此类推, 一直到 达第 1层。
球形译码算法能够获得与最大似然检测方法一样的性能, 但球形译码 算法的复杂度受信噪比的影响较大, 在低信噪比的情况下, 球形译码的复 杂度将会很高。
K-Best算法相对于最大似然检测方法是一种次优的方法, 其复杂度只 与每层保留的节点数量有关, 不受信噪比的影响。
在进行信号检测时, 如何结合球形译码算法和 K-Best算法的特点, 使 信号检测的复杂度和准确性兼顾, 成为需要解决的问题。 发明内容
有鉴于此, 本发明的主要目的在于提供一种多输入多输出系统的信号 检测方法和装置, 可以实现在不同的信噪比的情况下, 釆用合适的检测算 法来实现 MIMO系统的信号检测。
为达到上述目的, 本发明的技术方案是这样实现的:
本发明提供的一种多输入多输出系统的信号检测方法, 设定信噪比阔 值; 该方法还包括:
获取当前信道的信噪比, 当信噪比大于所述信噪比阈值时, 釆用球形 译码算法进行信号检测; 当信噪比不大于所述信噪比阔值时, 釆用 K-Best 算法进行信号检测。
上述方案中, 所述设定信噪比阔值为: 对当前信道在各个信噪比条件 下, 统计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的 运算次数超过并最接近 K-Best 算法的运算次数时的信噪比作为信噪比阔 值。
上述方案中, 所述釆用球形译码算法进行信号检测为: 设定初始搜索 半径以及最大搜索半径; 按照检测半径进行球形译码算法的信号检测, 在 检测成功时, 结束流程; 在检测不成功时, 增大检测半径, 并判断增大后 的半径是否大于最大搜索半径,在大于时,釆用 K-Best算法进行信号检测; 在不大于时,, 按照增大后的检测半径进行球形译码算法的信号检测。
上述方案中, 所述釆用球形译码算法进行信号检测为: 釆用相对最大 似然检测次优的算法得到一个次优解, 将所述次优解与接收信号向量的距 离作为初始搜索半径进行球形译码算法的信号检测。
上述方案中, 在设定初始搜索半径以及最大搜索半径时, 该方法进一 步包括: 设定最大搜索节点数, 在所述按照检测半径进行球形译码算法的 信号检测时, 进行搜索访问到的节点数的判断, 当搜索访问到的节点数达 到设定的最大搜索节点数时, 输出与接收信号向量最近的格点作为检测结 果; 当搜索访问到的节点数达到设定的最大搜索节点数、 且仍没有搜索到 格点时, 釆用 K-Best算法进行信号检测。
本发明提供的一种多输入多输出系统的信号检测装置, 该装置包括: 信噪比阔值模块、 信噪比模块、 比较模块、 K-Best 算法模块、 球形译码算 法模块; 其中,
信噪比阔值模块, 用于设定信噪比阔值;
信噪比模块, 用于获取当前信道的信噪比;
比较模块, 用于当信噪比大于信噪比阔值时, 通知球形译码算法模块; 当信噪比不大于信噪比阔值时, 通知 K-Best算法模块;
K-Best算法模块, 用于釆用 K-Best算法进行信号检测; 球形译码算法模块, 用于釆用球形译码算法进行信号检测。 上述方案中, 所述球形译码算法模块, 具体用于设定初始搜索半径以 及最大搜索半径; 按照检测半径进行球形译码算法的信号检测, 在检测成 功时, 结束操作; 在检测不成功时, 增大检测半径, 并判断增大后的半径 是否大于最大搜索半径, 在大于时, 通知 K-Best算法模块; 否则, 按照增 大后的检测半径进行球形译码算法的信号检测。
上述方案中, 所述球形译码算法模块, 进一步用于釆用相对最大似然 检测次优的算法得到一个次优解, 将所述次优解与接收信号向量的距离作 为初始搜索半径。
上述方案中, 所述球形译码算法模块在检测成功时, 输出最大似然解 作为检测结果; 或者, 除了输出最大似然解以外, 还输出距离最大似然解 最近的解, 得到发送比特的软信息。
上述方案中, 所述球形译码算法模块, 还用于设定最大搜索节点数; 在按照检测半径进行球形译码算法的信号检测时, 进行搜索访问到的节点 数的判断, 当搜索访问到的节点数达到设定的最大搜索节点数时, 输出与 接收信号向量最近的格点作为检测结果; 当搜索访问到的节点数达到设定 的最大搜索节点数、 且仍没有搜索到格点时, 通知 K-Best算法模块。
本发明提供的一种 MIMO系统的信号检测方法和装置, 设定信噪比阔 值; 获取当前信道的信噪比, 当信噪比大于所述信噪比阈值时, 釆用球形 译码算法进行信号的检测; 当信噪比不大于所述信噪比阔值时,釆用 K-Best 算法进行信号的检测; 如此, 可以实现在不同的信噪比的情况下, 釆用合 适的检测算法来实现 MIMO系统的信号检测。 附图说明
图 1为 MIMO系统模型示意图;
图 2为本发明实现一种 MIMO系统的信号检测方法流程的示意图; 图 3为本发明实现一种 MIM0系统的信号检测装置结构的示意图; 图 4为实施例一实现一种 MIM0系统的信号检测方法流程的示意图; 图 5为实施例二实现一种 MIMO系统的信号检测方法流程的示意图。 具体实施方式
现有技术中, 在进行信号检测时只釆用上述的一种算法, 这样导致在 信噪比的大小不同时, 信号检测的复杂度和准确性不能兼顾。 如: 在信噪 比小于某一值时, 釆用球形译码算法的运算次数将远远大于 K-Best算法的 运算次数, 为信号检测在增加准确性的同时却增加了很大的复杂度; 而在 信噪比大于某一值时, 釆用 K-Best算法的运算次数将远远大于球形译码算 法的运算次数, 会为信号检测在降低准确性的同时增加了很大的复杂度。
本发明的基本思想是: 设定信噪比阈值; 获取当前信道的信噪比, 当 信噪比大于所述信噪比阈值时, 釆用球形译码算法进行信号检测; 当信噪 比不大于所述信噪比阔值时, 釆用 K-Best算法进行信号检测。
本发明实现一种 MIMO系统的信号检测方法, 如图 2所示, 该方法包 括以下几个步骤:
步骤 201 : 设定信噪比阔值;
具体的, 对当前信道在各个信噪比条件下, 统计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的运算次数超过并最接近 K-Best 算法的运算次数时的信噪比作为信噪比阔值;
进一步的, 本步骤中, 在设定信噪比阈值后, 根据检测过程中信道的 条件, 仍可对信噪比阔值进行自适应调整。
步骤 202: 获取当前信道的信噪比, 判断所述信噪比是否大于信噪比阔 值, 如果不大于, 则执行步骤 206; 如果大于, 则执行步骤 203;
本步骤所述信噪比的获取方法为现有技术, 这里不再赘述。
步骤 203: 设定球形译码算法的初始搜索半径以及最大搜索半径; 本步骤中, 所述初始搜索半径可以由式(7 )得到; 也可以釆用相对最 大似然检测次优的算法得到一个次优解 § , 将所述次优解与接收信号向量 y 的距离作为初始搜索半径, 如下式所示:
此时在半径 d 内至少存在一个格点, 所以不需要设定最大搜索半径, 所述相对最大似然检测次优的算法包括: K-Best算法、 迫零算法、 最小均 方误差算法等;
所述最大搜索半径也可以通过下式得到:
y
H
其中, 为最大半径系数, n 为两倍发s <射天线数, σ2为噪声方差, 且 满足 ≥"。
进一步的, 本步骤还包括设定最大搜索节点数。
步骤 204:按照检测半径进行球形译码算法的信号检测,在检测成功时, 结束流程; 在检测不成功时, 执行步骤 205;
本步骤中, 在第一次进行球形译码算法的信号检测时, 所述检测半径 为设定的初始搜索半径。
进一步的, 所述在检测成功时, 由球形译码算法输出的检测结果可以 釆用硬判决的方法, 即输出最大似然解作为检测结果; 也可以釆用软输出 的方法, 即除了输出最大似然解以外, 还输出距离最大似然解最近的若干 个解, 利用这些解计算发送比特的软信息。
进一步的, 如果在步骤 203 中设定了最大搜索节点数, 则所述按照检 测半径进行球形译码算法的信号检测时, 进行搜索访问到的节点数的判断, 当搜索访问到的节点数达到设定的最大搜索节点数时, 输出与接收信号向 量最近的格点作为检测结果, 结束流程; 当搜索访问到的节点数达到设定 的最大搜索节点数、 且仍没有得到格点时, 执行步骤 206。 步骤 205:增大检测半径,并判断增大后的半径是否大于最大搜索半径, 在大于时, 执行步骤 206; 否则, 执行步骤 204;
本步骤中, 所述增大检测的半径的方法, 可以是在初始搜索半径和最 大搜索半径之间线性增大、 也可以是通过折半的方法增大半径, 即增大后 的半径取当前半径与最大搜索半径的中间值, 等等。
步骤 206: 釆用 K-Best算法进行信号检测, 结束流程;
本步骤进一步包括: 由于 K-Best算法输出的检测结果不一定是最大似 然解, 通常得到的是最大似然解附近的解, 为了进一步提高 K-Best算法检 测的性能, 可以釆用软输出的方法, 即在釆用 K-Best算法检测到达第一层 的时候, 输出最优的 K个解进行比特软信息的计算, 所述 K为自然数。
为了实现上述方法,本发明还提供了一种 MIMO系统的信号检测装置, 如图 3所示, 该装置包括: 信噪比阔值模块 31、 信噪比模块 32、 比较模块 33、 K-Best算法模块 34、 球形译码算法模块 35; 其中,
信噪比阔值模块 31 , 用于设定信噪比阔值;
具体的, 所述信噪比阔值模块 31对当前信道在各个信噪比条件下, 统 计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的运算次 数超过并最接近 K-Best算法的运算次数时的信噪比作为信噪比阔值;
信噪比模块 32, 用于获取当前信道的信噪比;
比较模块 33 , 用于当信噪比大于信噪比阔值时, 通知球形译码算法模 块 35; 当信噪比不大于信噪比阔值时, 通知 K-Best算法模块 34;
K-Best算法模块 34, 用于釆用 K-Best算法进行信号检测;
所述 K-Best算法模块 34, 进一步用于在釆用 K-Best算法检测到达第 一层的时候,输出最优的 K个解进行比特软信息的计算,所述 K为自然数。
球形译码算法模块 35 , 用于釆用球形译码算法进行信号检测; 具体的, 所述球形译码算法模块 35设定初始搜索半径以及最大搜索半 径; 按照检测半径进行球形译码算法的信号检测, 在检测成功时, 结束操 作; 在检测不成功时, 增大检测半径, 并判断增大后的半径是否大于最大 搜索半径, 在大于时, 通知 K-Best算法模块 34; 否则, 按照增大后的检测 半径进行球形译码算法的信号检测。 其中, 在第一次进行球形译码算法的 信号检测时, 所述检测半径为初始搜索半径。
进一步的, 所述球形译码算法模块 35设定初始搜索半径时, 釆用相对 最大似然检测次优的算法得到一个次优解, 将所述次优解与接收信号向量 的距离作为初始搜索半径后, 球形译码算法模块 35不需要再设定最大搜索 半径;
进一步的, 所述球形译码算法模块 35在检测成功时, 输出的检测结果 可以釆用硬判决的方法, 即输出最大似然解作为检测结果; 也可以釆用软 输出的方法, 即除了输出最大似然解以外, 还输出距离最大似然解最近的 若干个解, 利用这些解得到发送比特的软信息;
进一步的, 所述球形译码算法模块 35 , 还用于设定最大搜索节点数; 在按照检测半径进行球形译码算法的信号检测时, 进行搜索访问到的节点 数的判断, 当搜索访问到的节点数达到设定的最大搜索节点数时, 输出与 接收信号向量最近的格点作为检测结果, 结束操作; 当搜索访问到的节点 数达到设定的最大搜索节点数、 且仍没有得到格点时, 通知 K-Best算法模 块 34。 实施例一: 釆用次优解与接收信号向量 y的距离作为球形译码的初始 搜索半径, 本发明实现一种 MIMO系统的信号检测方法, 如图 4所示, 该 方法包括以下几个步骤:
步骤 401 : 设定信噪比阔值;
具体的, 对当前信道在各个信噪比条件下, 统计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的运算次数超过并最接近 K-Best 算法的运算次数时的信噪比作为信噪比阔值;
步骤 402: 获取当前信道的信噪比, 判断所述信噪比是否大于信噪比阔 值, 如果不大于, 则执行步骤 405; 如果大于, 则执行步骤 403;
步骤 403: 设定球形译码算法的初始搜索半径;
具体的, 釆用相对最大似然检测次优的算法得到一个次优解 将所述 次优解与接收信号向量 y的距离作为初始搜索半径, 如下式所示:
d = - H s
所述相对最大似然检测次优的算法包括: K-Best算法、 迫零算法、 最 小均方误差算法等。
步骤 404: 按照初始搜索半径进行球形译码算法的接收信号的检测, 结 束流程;
本步骤中, 由球形译码算法输出的检测结果可以釆用硬判决的方法, 即输出最大似然解作为检测结果; 也可以釆用软输出的方法, 即除了输出 最大似然解以外, 还输出距离最大似然解最近的若干个解, 利用这些解计 算发送比特的软信息。
步骤 405: 釆用 K-Best算法进行接收信号的检测, 结束流程; 本步骤进一步包括: 由于 K-Best算法输出的检测结果不一定是最大似 然解, 通常得到的是最大似然解附近的解, 为了进一步提高 K-Best算法检 测的性能, 可以釆用软输出的方法, 即在釆用 K-Best算法检测到达第一层 的时候, 输出最优的 K个解进行比特软信息的计算, 所述 K为自然数。
实施例二: 设定球形译码算法中最大搜索节点数, 本发明实现一种 MIMO系统的信号检测方法, 如图 5所示, 该方法包括以下几个步骤: 步骤 501 : 设定信噪比阔值;
具体的, 对当前信道在各个信噪比条件下, 统计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的运算次数超过并最接近 K-Best 算法的运算次数时的信噪比作为信噪比阔值。
步骤 502: 获取当前信道的信噪比, 判断所述信噪比是否大于信噪比阔 值, 如果不大于, 则执行步骤 507; 如果大于, 则执行步骤 503;
步骤 503: 设定球形译码算法的初始搜索半径以及最大搜索节点数; 步骤 504:按照设定的初始搜索半径和最大搜索节点数进行球形译码算 法的接收信号的检测;
步骤 505: 当搜索访问到的节点数达到设定的最大搜索节点数时, 判断 是否得到格点; 若得到, 则执行步骤 506; 否则, 执行步骤 507;
步骤 506: 输出与接收信号向量最近的格点作为检测结果, 结束流程; 步骤 507: 釆用 K-Best算法进行接收信号的检测, 结束流程; 本步骤进一步包括: 由于 K-Best算法输出的检测结果不一定是最大似 然解, 通常得到的是最大似然解附近的解, 为了进一步提高 K-Best算法检 测的性能, 可以釆用软输出的方法, 即在釆用 K-Best算法检测到达第一层 的时候, 输出最优的 K个解进行比特软信息的计算, 所述 K为自然数。
以上所述, 仅为本发明的较佳实施例而已, 并非用于限定本发明的保 护范围, 凡在本发明的精神和原则之内所作的任何修改、 等同替换和改进 等, 均应包含在本发明的保护范围之内。

Claims

权利要求书
1、 一种多输入多输出系统的信号检测方法, 其特征在于, 设定信噪比 阔值; 该方法还包括:
获取当前信道的信噪比, 当信噪比大于所述信噪比阈值时, 釆用球形 译码算法进行信号检测; 当信噪比不大于所述信噪比阔值时, 釆用 K-Best 算法进行信号检测。
2、 根据权利要求 1所述的信号检测方法, 其特征在于, 所述设定信噪 比阔值为: 对当前信道在各个信噪比条件下, 统计得到球形译码算法与 K-Best算法的运算次数, 以球形译码算法的运算次数超过并最接近 K-Best 算法的运算次数时的信噪比作为信噪比阔值。
3、 根据权利要求 1所述的信号检测方法, 其特征在于, 所述釆用球形 译码算法进行信号检测为: 设定初始搜索半径以及最大搜索半径; 按照检 测半径进行球形译码算法的信号检测, 在检测成功时, 结束流程; 在检测 不成功时, 增大检测半径, 并判断增大后的半径是否大于最大搜索半径, 在大于时, 釆用 K-Best算法进行信号检测; 否则, 按照增大后的检测半径 进行球形译码算法的信号检测。
4、 根据权利要求 1所述的信号检测方法, 其特征在于, 所述釆用球形 译码算法进行信号检测为: 釆用相对最大似然检测次优的算法得到一个次 优解, 将所述次优解与接收信号向量的距离作为初始搜索半径进行球形译 码算法的信号检测。
5、 根据权利要求 3所述的信号检测方法, 其特征在于, 在设定初始搜 索半径以及最大搜索半径时, 该方法进一步包括: 设定最大搜索节点数, 在所述按照检测半径进行球形译码算法的信号检测时, 进行搜索访问到的 节点数的判断, 当搜索访问到的节点数达到设定的最大搜索节点数时, 输 出与接收信号向量最近的格点作为检测结果; 当搜索访问到的节点数达到 设定的最大搜索节点数、 且仍没有搜索到格点时, 釆用 K-Best算法进行信 号检测。
6、一种多输入多输出系统的信号检测装置,其特征在于,该装置包括: 信噪比阔值模块、 信噪比模块、 比较模块、 K-Best 算法模块、 球形译码算 法模块; 其中,
信噪比阔值模块, 用于设定信噪比阔值;
信噪比模块, 用于获取当前信道的信噪比;
比较模块, 用于当信噪比大于信噪比阔值时, 通知球形译码算法模块; 当信噪比不大于信噪比阔值时, 通知 K-Best算法模块;
K-Best算法模块, 用于釆用 K-Best算法进行信号检测;
球形译码算法模块, 用于釆用球形译码算法进行信号检测。
7、 根据权利要求 6所述的信号检测装置, 其特征在于, 所述球形译码 算法模块, 具体用于设定初始搜索半径以及最大搜索半径; 按照检测半径 进行球形译码算法的信号检测, 在检测成功时, 结束操作; 在检测不成功 时, 增大检测半径, 并判断增大后的半径是否大于最大搜索半径, 在大于 时, 通知 K-Best算法模块; 在不大于时, 按照增大后的检测半径进行球形 译码算法的信号检测。
8、 根据权利要求 6所述的信号检测装置, 其特征在于, 所述球形译码 算法模块, 进一步用于釆用相对最大似然检测次优的算法得到一个次优解, 将所述次优解与接收信号向量的距离作为初始搜索半径。
9、 根据权利要求 6所述的信号检测装置, 其特征在于, 所述球形译码 算法模块, 进一步用于在检测成功时, 输出最大似然解作为检测结果; 或 者, 除了输出最大似然解以外, 还输出距离最大似然解最近的解, 得到发 送比特的软信息。
10、 根据权利要求 6所述的信号检测装置, 其特征在于, 所述球形译 码算法模块, 还用于设定最大搜索节点数; 在按照检测半径进行球形译码 算法的信号检测时, 进行搜索访问到的节点数的判断, 当搜索访问到的节 点数达到设定的最大搜索节点数时, 输出与接收信号向量最近的格点作为 检测结果; 当搜索访问到的节点数达到设定的最大搜索节点数、 且仍没有 搜索到格点时, 通知 K-Best算法模块。
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102638336A (zh) * 2012-04-28 2012-08-15 电子科技大学 一种固定复杂度的mimo接收机信号搜索球形译码算法
WO2015047428A1 (en) * 2013-09-27 2015-04-02 Intel Corporation Channel-adaptive configurable mimo detector for multi-mode wireless systems
CN108418660A (zh) * 2018-02-13 2018-08-17 桂林电子科技大学 一种低信噪比环境中提高特征值信号检测灵敏度的方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103888217B (zh) * 2012-12-24 2017-11-14 中兴通讯股份有限公司 一种球形译码检测方法及装置
CN104038457A (zh) * 2014-06-26 2014-09-10 西安交通大学 编码mimo系统中基于初始球半径的软输出球形译码方法
CN104038269A (zh) * 2014-06-26 2014-09-10 西安交通大学 一种提高高阶mimo系统中吞吐量稳定性的方法
CN107995141B (zh) * 2017-10-23 2020-08-04 中国人民解放军信息工程大学 一种fbmc-oqam系统的载波调制方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101345592A (zh) * 2008-08-21 2009-01-14 上海交通大学 应用于mimo的自适应信号的检测器及检测方法
CN101414992A (zh) * 2007-10-19 2009-04-22 富士通株式会社 Mimo无线通信系统
CN101674160A (zh) * 2009-10-22 2010-03-17 复旦大学 多输入多输出无线通信系统信号检测方法及装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2405322A1 (en) * 2001-09-28 2003-03-28 Telecommunications Research Laboratories Channel code decoding for the cdma forward link
CN101192898A (zh) * 2006-12-01 2008-06-04 北京三星通信技术研究有限公司 多天线信号干扰抵消检测方法和设备

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101414992A (zh) * 2007-10-19 2009-04-22 富士通株式会社 Mimo无线通信系统
CN101345592A (zh) * 2008-08-21 2009-01-14 上海交通大学 应用于mimo的自适应信号的检测器及检测方法
CN101674160A (zh) * 2009-10-22 2010-03-17 复旦大学 多输入多输出无线通信系统信号检测方法及装置

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102638336A (zh) * 2012-04-28 2012-08-15 电子科技大学 一种固定复杂度的mimo接收机信号搜索球形译码算法
CN102638336B (zh) * 2012-04-28 2014-07-09 电子科技大学 一种固定复杂度的mimo接收机信号搜索球形译码算法
WO2015047428A1 (en) * 2013-09-27 2015-04-02 Intel Corporation Channel-adaptive configurable mimo detector for multi-mode wireless systems
CN108418660A (zh) * 2018-02-13 2018-08-17 桂林电子科技大学 一种低信噪比环境中提高特征值信号检测灵敏度的方法
CN108418660B (zh) * 2018-02-13 2020-11-06 桂林电子科技大学 一种低信噪比环境中提高特征值信号检测灵敏度的方法

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