WO2018161654A1 - 一种scma码本盲估计方法 - Google Patents

一种scma码本盲估计方法 Download PDF

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WO2018161654A1
WO2018161654A1 PCT/CN2017/114303 CN2017114303W WO2018161654A1 WO 2018161654 A1 WO2018161654 A1 WO 2018161654A1 CN 2017114303 W CN2017114303 W CN 2017114303W WO 2018161654 A1 WO2018161654 A1 WO 2018161654A1
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codebook
node
function
resource block
variable
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French (fr)
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杨延军
赵玉萍
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北京大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H04L1/0055—MAP-decoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
    • H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
    • H04L27/38—Demodulator circuits; Receiver circuits

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  • the invention belongs to the field of digital communication, and relates to a SCMA (Sparse Code Multiple Access) codebook blind estimation method, and particularly relates to an estimation method for a codebook usage situation in the process of SCMA access.
  • SCMA Sese Code Multiple Access
  • SCMA is a new type of multiple access technology based on spread spectrum coding. It combines low density coding (LDS, Low Density Signature) and multidimensional constellation modulation. By selecting different codebooks, different users can be Access in case of handover. Under the same resource conditions, SCMA technology can support more user connections, even surpassing the spread ratio of traditional CDMA technology, so it has a good prospect in IoT applications that require massive connections.
  • LDS low density coding
  • SCMA Low Density Signature
  • the research on SCMA technology in the literature mostly assumes that the distribution of the user equipment (User Equipment) is known to the receiver, and then uses the sub-optimal MPA (Message Passing Algorithm) to send data to the user. Perform demodulation. If the codebook distribution to the UE is unknown, the JMPA (Joint MPA) algorithm combined with codebook detection must be used, which not only increases the complexity of the MPA algorithm, but also cannot handle the case where multiple UEs select the same codebook. .
  • JMPA Joint MPA
  • an object of the present invention is to provide a blind estimation method for detecting a codebook usage situation of a UE in an uplink channel in an SCMA scheme.
  • the method requires the UE to send a small piece of certain information as a preamble before transmitting its own data. There is no special requirement for the design of the preamble, and any bit stream can be used as the preamble. If the modulation mode selected by the sender is not a constant envelope, you can choose a constellation point with a large energy when designing the preamble.
  • the receiving end uses the MPA algorithm to estimate the codebook usage of the UE by using the preamble information, that is, whether each codebook is used by the user, and the number of users using the codebook is.
  • the estimation information of the codebook can simplify the subsequent decoding process, and reduce the calculation amount of the decoding algorithm without degrading the decoding performance, but the subsequent decoding scheme is not the core content of the present invention.
  • the method can estimate the codebook, and can use the same codebook for different users. The detection is performed, the codebook conflict is found as early as possible, and the subsequent decoding is provided, but how to use the estimation result of the present invention is not the core content of the present invention.
  • the SCMA technology is an alternative to the 5G communication.
  • the main application scenario is mobile communication.
  • the following examples and descriptions of the present invention are performed under the framework of the LTE protocol unless otherwise specified.
  • the codebook blind estimation method proposed by the present invention can be used to estimate the codebook usage of the UE.
  • the technical solution of the present invention is:
  • each UE needs to first obtain uplink synchronization according to the reference signal of the downlink channel.
  • the foregoing preamble is first sent in the fixed time-frequency resource block specified by the protocol, and then the first preamble is sent.
  • the transmitted preamble content may be any known sequence, and the sequence length is at least one symbol length, and the length of the preamble sequence is determined according to the requirement for estimation accuracy.
  • the receiver may receive a superposition of preambles transmitted by multiple UEs in a specified time-frequency resource block, and may use the MPA algorithm for the UE code for each received symbol.
  • the distribution of this case is estimated, and the specific algorithm details will be described in detail later.
  • each received symbol can estimate the value of the confidence of a codebook distribution.
  • the length of the current pilot code is multiple symbols, multiple The estimation results are superimposed to improve the accuracy of the codebook estimation.
  • step 2 it is assumed that all the channels of the UE to the receiving end are AWGN channels, and the UE can adjust the transmitting power according to the reference signal of the downlink channel to ensure that the signal power of the signals transmitted by the UEs reaches the receiver is substantially equal.
  • the MPA algorithm can adjust the estimated range of the codebook according to the actual UE deployment situation. For example, it can be defined that each codebook has a maximum of 2 UE selections, and at least no UE selection.
  • the SCMA codebook distribution estimation algorithm proposed by the present invention divides the demodulation of SCMA into two parts: codebook estimation and codeword demodulation.
  • the invention mainly relates to the part of the codebook estimation, and the estimation of the codebook usage alone obtains the simplification of the decoding algorithm at the cost of reducing the spectral efficiency, and can reduce the bit error rate due to the codebook conflict to some extent. .
  • Figure 1 is a flow chart of the method of the present invention
  • Figure 3 is a constellation diagram used in an example of the present invention.
  • FIG. 4 is a diagram showing a case where different codebook selection constellations are illustrated in the example of the present invention.
  • Figure 5 is a diagram showing the relationship between the length of the preamble and the estimation accuracy.
  • FIG. 1 The flow of the present invention is shown in FIG. 1.
  • the estimation algorithm described in the present invention will be clearly and completely described below in conjunction with an example. It is to be understood that the described examples are only a part of the embodiments of the invention, rather than all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.
  • the preset conditions for this example are as follows:
  • the resource mapping of SCMA can be represented by a factor graph. As shown in FIG. 2, there are a total of 6 codebooks, occupying 4 resource blocks.
  • Figure 2 is a factor graph, the codebook (circular node) is a variable node, and the resource block (square node) is a function node. The inclusion of a line between the codebook node and the resource block indicates that the codebook will occupy the resource block to send data.
  • the factor graph shown in Fig. 2 transmits data on two resource blocks for each codebook, and each resource block contains data stacks of three codebooks.
  • the UE uses the QPSK scheme to modulate the transmitted preamble.
  • One symbol contains two binary bits.
  • each resource block has a maximum of three QPSK symbols superimposed according to the structure of the factor graph.
  • the decoding unit of each SCMA is 4 received symbols, and corresponds to the transmission data of 6 users in the absence of a codebook collision.
  • the constellation used by the UE is rotated appropriately so that the rotation angles of the QPSK symbols superimposed on the resource blocks are different.
  • 3 is a three constellation diagram used in the present example, and the constellation diagrams No. 1 and No. 2 are rotated by ⁇ /6 and ⁇ /3, respectively, for the constellation diagram No. 0.
  • Figure 4 shows the constellation diagram selected for each codebook at the time of encoding.
  • the two numbers above the codebook node indicate the constellation sequence number used by the codebook on different resource blocks, and the number below the resource node is the constellation sequence number used by the three symbols that may be superimposed on this node. Sequence and resources of serial numbers in the figure The order of nodes and codebook nodes is the same.
  • Six codebook nodes correspond to six variable nodes, and four received symbols correspond to four function nodes.
  • the initial confidence vector assumes that all codebook selections are equally probable, ie for each codebook, there may be three cases: no UE selection; one UE selection; two UE selections. In the initial case, the probability of each case is 1/3.
  • the message passed in the factor graph is transmitted bidirectionally along the edge of the factor graph, and the content passed is the probability that the variable nodes have different values.
  • this probability is a 3-dimensional vector Representing this variable node (ie, the corresponding codebook) has no probability of UE selection, 1 UE selection, and 2 UE selections.
  • the footmark k in the formula indicates the corresponding kth variable node, and the footmark n represents the nth function node.
  • a possible value of the number, m is the number of possible values, that is, when m is 3, the selection range of q jm is 0, 1, and 2. among them Represents the symbol R n received on the function node F n and the data vector sent in each case (using Indicates the Euclidean distance between). Multiplication section to traverse all the nodes connected to the function of the other variable nodes F n V j, and summing section for a combination where the value of all the nodes (i.e., codebook selection cases) is traversed.
  • step 3 Similar to step 3, the message sent from the variable node to the function node The same is the value probability vector of the variable node. It summarizes the messages (ie, probabilities) sent by other function nodes, but since they are separate events, the final result is multiplied by these probabilities. Similarly, if you calculate in the logarithmic domain, multiplication can be simplified to addition:
  • the iterative process repeats the contents of steps 3 and 4 until the agreed maximum number of iterations is reached.
  • the final value probability value is obtained according to the message received on the variable node, and the number of users with the largest probability value is selected as the result output of the algorithm.
  • the final result is a 6-dimensional vector, each element representing the number of users included in each codebook.
  • Figure 5 shows the performance curves obtained by simulation.
  • the simulation condition is based on the factor graph of Figure 1. It is assumed that there are 7 users transmitting data at the same time.
  • One of the codebooks has 2 UE selections, and the remaining codebooks have only one UE selection.
  • the three curve distributions in the simulation results represent the preamble.
  • the error probability of detection under different SNR conditions when the code length is 2, 4, and 6 bit lengths.
  • the MPA algorithm used in the simulation is calculated using the logarithmic domain, and the highest number of iterations is set to 5.
  • the error probability obtained by the log-domain method is slightly higher than that of the non-logarithmic domain, but the simulation speed is significantly improved.

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

本发明公开了一种SCMA码本盲估计方法。本方法为：1）使用因子图表示SCMA的资源映射情况，其中，将每一码本作为一变量节点，每一资源块作为一函数节点，码本和资源块之间包含连线表示该码本将占用与其连接的资源块发送数据；为每一码本设置一初始置信度向量；2）用户设备UE在LTE协议规定的固定时频资源块发送自己的数据之前，先发送一前导码信息；3）接收端根据指定时频资源块内收到的前导码信息和该因子图对码本使用情况进行估计，估计出每一码本是否有用户使用以及使用该码本的用户数。本发明在存在码本冲突的情况下，可以将未冲突部分的用户数据成功解码。

Description

一种SCMA码本盲估计方法 技术领域
本发明属于数字通信领域，涉及一种SCMA(Sparse Code Multiple Access，稀疏编码多址接入)码本盲估计方法，特别涉及在SCMA接入的过程中对码本使用情况的估计方法。
背景技术
SCMA是一种基于扩频编码的新型多址接入技术，它结合了低密度编码(LDS，Low Density Signature)和多维星座图调制，通过选择不同的码本，使不同的用户可以在非正交情况下进行接入。在同样的资源条件下，SCMA技术可以支持更多的用户连接，甚至超过传统CDMA技术的扩频比，因此在需要海量连接的物联网应用中有很好的前景。
目前在文献中对SCMA技术的研究大多假设接收端对UE(User Equipment，用户设备)码本的分布情况已知，然后利用次优的MPA(Message Passing Algorithm，消息传递算法)对用户的发送数据进行解调。如果对UE的码本分布未知，则必须使用结合了码本探测的JMPA(联合MPA)算法，这不仅增加了MPA算法的复杂度，而且无法对多个UE选择了同样码本的情况进行处理。
在实际的系统中，UE的个数和发送数据的时间都是未知的，因此在SCMA解调的过程中，有哪些码本被使用，是否有多个UE选择了同一个码本，这些信息都要在解调的过程中进行估计。
发明内容
针对现有技术中存在的技术问题，本发明的目的在于提供了一种SCMA方案中在上行信道对UE的码本使用情况进行探测的盲估计方法。本方法需要UE在发送自己的数据之前，先发送一小段确定信息作为前导码。前导码的设计并没有特殊的要求，可以使用任意的比特流作为前导码。如果发送端选择的调制方式不是恒包络，可以在设计前导码时尽量选择能量大的星座点。
接收端利用前导码信息使用MPA算法对UE的码本使用情况进行估计，即估计出每一个码本是否有用户使用，使用这个码本的用户数是多少。码本的估计信息可以对后续的解码过程进行简化，在不降低解码性能的情况下减少解码算法的计算量，但后续的解码方案不是本发明的核心内容。本方法对码本进行估计的结果，可以对不同的用户采用同样码本的情 况进行检测，尽早的发现码本冲突，并对后续的解码提供依据，但如何使用本发明的估计结果不是本发明的核心内容。
SCMA技术作为5G通信的一个备选方案，主要的应用场景是移动通信，本发明后续的示例和说明在不特别指出的前提下都是在LTE协议的框架下进行。在移动通信的随机接入过程中，由于基站对UE的情况都是未知的，可以使用本发明提出的码本盲估计方法对UE的码本使用情况进行估计。为了实现上述目的，本发明的技术方案是：
1.在发送端，每个UE都需要首先根据下行信道的参考信号获得上行的同步，当有数据要发送时，在协议规定的固定时频资源块首先发送前述的前导码，然后再发送自己的数据。发送的前导码内容可以是任意已知序列，序列长度最短为一个符号的长度，前导码序列的长度根据对估计准确率的需求决定。
2.在接收端，由于随机接入的特性，接收机在指定的时频资源块可能会收到多个UE发送前导码的叠加，对于每个接收到的符号都可以利用MPA算法对UE码本的分布情况进行估计，具体的算法细节将在后面详细说明。
3.如果UE前导码的长度为一个符号，根据步骤2，每个接收符号都可以估计出一个码本分布情况的置信度的值，当前导码的长度为多个符号时，可以将多个估计结果进行叠加，来提高码本估计的准确度。
所述步骤2中，假定所有UE到接收端的信道都是AWGN信道，UE可以根据下行信道的参考信号调整发射功率，以保证各UE发送的信号到达接收机的信号功率大致相等。
所述步骤2中，MPA算法可以根据实际UE部署情况，调整码本的估计范围。例如可以限定每个码本最多会有2个UE选择，最少是没有UE选择。
本发明提出的SCMA码本分布情况估计算法将SCMA的解调分成了两个部分：码本估计和码字解调。本发明主要涉及码本估计的部分，单独对码本使用情况进行估计是以降低频谱效率的代价获得了解码算法的简化，并可以从一定程度上降低由于码本冲突而导致的误码率上升。
与现有技术相比，本发明的积极效果为：
1.提出了一种新的码本适用情况估计算法。
2.在存在码本冲突的情况下，可以将未冲突部分的用户数据成功解码。
附图说明
图1是本发明的方法流程图；
图2是本发明实例使用的SCMA方案的因子图；
图3是本发明实例使用的星座图；
(a)为0号星座图，(b)为1号星座图，(c)为2号星座图；
图4是本发明实例不同码本选择星座图的情况图；
图5是前导码长度和估计准确率的关系图。
具体实施方式
本发明的流程如图1所示，下面将结合一个实例，对本发明所描述的估计算法进行清楚、完整的描述。可以理解的是，所描述的实例仅仅是本发明的一部分实例，而不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本实例的预设条件如下：
1.SCMA的资源映射情况可以使用因子图来表示。如图2所示，一共有6个码本，占用4个资源块。图2是一个因子图，码本(圆形节点)是变量节点，资源块(方形节点)是函数节点。码本节点和资源块之间包含连线表示这个码本将占用这个资源块发送数据。图2表示的因子图每个码本都在2个资源块上发送数据，而每个资源块都包含3个码本的数据叠加。
2.UE使用QPSK方案对发送的前导码进行调制，一个符号包含两个二进制位，在没有码本冲突的情况下，根据因子图的结构，每个资源块最多有3个QPSK符号的叠加。
3.假定最多有2个用户选择同样的码本
上面所述条件1中，每个SCMA的解码单元是4个接收符号，在没有码本冲突的情况下最多对应6个用户的发送数据。
上面所述条件2中，UE使用的星座图会进行适当的旋转，使得在资源块上叠加的QPSK符号的旋转角度各不相同。图3是本实例中使用的3个星座图，1号和2号星座图分别对0号星座图旋转了π/6和π/3。图4表示了每个码本在编码时选择的星座图情况。码本节点上面的两个数字表明了这个码本在不同资源块上使用的星座图序号，而资源节点下面的数字是在这个节点上可能叠加的三个符号所使用的星座图序号。图中序号的顺序和资源 节点及码本节点的排列顺序相同。
现根据接收符号和因子图，将具体的码本估计MPA算法描述如下：
1. 6个码本节点对应6个变量节点，4个接收符号对应4个函数节点。
2.初始的置信度向量假定所有的码本选择都是等概率的，即对于每个码本，都可能有三种情况：没有UE选择；有1个UE选择；有2个UE选择。初始情况每种情况的概率都是1/3。
3.由函数节点发送到变量节点的消息如下面的公式表示
Figure PCTCN2017114303-appb-000001
在因子图中传递的消息沿因子图的边双向传递，传递的内容是变量节点不同取值的概率。对于本实例中的情况，这个概率是一个3维的向量
Figure PCTCN2017114303-appb-000002
分别代表这个变量节点(即对应的码本)没有UE选择、有1个UE选择和有2个UE选择的概率。公式中的脚标k表示对应第k个变量节点，脚标n代表第n个函数节点。
上面公式中
Figure PCTCN2017114303-appb-000003
代表对于第k个变量节点Vk(即第k个码本)，在第l次迭代的时候有qki个UE选择了这个码本的概率。这个概率是由第n个函数节点Fn发送到第k个变量节点Vk的消息，它根据从除了Vk的其它连接到函数节点Fn的变量节点Vj发送过来的消息
Figure PCTCN2017114303-appb-000004
计算得来。计算方法就是公式等号右边的部分，它根据全概公式计算了在k节点上k变量的值为qki的时候，其它节点的全部可能情况的概率叠加，qjm是选择j码本的用户数的一种可能值，m为这个可能值的数目，即当m为3的时候，qjm的选择范围是0、1和2。其中
Figure PCTCN2017114303-appb-000005
代表函数节点Fn上接收到的符号Rn与每种情况所发的数据矢量(用
Figure PCTCN2017114303-appb-000006
表示)之间的欧几里德距离。乘法部分要对所有的连接到这个函数节点Fn的其他变量节点Vj进行遍历，而求和部分要对所有这些节点的取值情况(即码本选择情况)的组合进行遍历。
如果在对数域进行计算，并利用Jacobian公式log(ea+eb)≈max(a,b)，就可以把上面的公式简化为：
Figure PCTCN2017114303-appb-000007
4.由变量节点发送到函数节点的消息如下面的公式表示
Figure PCTCN2017114303-appb-000008
与步骤3类似，从变量节点发送到函数节点的消息
Figure PCTCN2017114303-appb-000009
同样是变量节点的取值概率向量。它汇总了其他函数节点发送过来的消息(即概率)，但由于分别是独立事件，因此最终结果由这些概率相乘获得。同样如果在对数域进行计算，乘法可以简化为加法：
Figure PCTCN2017114303-appb-000010
5.迭代过程重复步骤3和步骤4的内容，直到达到约定的最高迭代次数。
6.最后根据变量节点上收到的消息获得最终的取值概率值，选取概率值最大的用户数作为本算法的结果输出。最后得到的结果是一个6维矢量，每个元素代表了在各个码本上包含的用户数目。
图5为通过仿真获得的性能曲线。仿真的条件是根据图1的因子图，假定有7个用户同时发送数据，其中一个码本有2个UE选择，其余码本都只有1个UE选择，仿真结果中的三条曲线分布代表了前导码长度为2个、4个和6个比特长度的时候在不同信噪比条件下的检测错误概率。仿真时使用的MPA算法采用了对数域进行计算，最高的迭代次数设定为5。采用对数域方法获得的错误概率略高于非对数域的方法，但仿真速度有显著的提高。
以上通过实施例描述了本发明所提供的一种SCMA系统中的上行信道对码本使用情况的估计方法，本领域的技术人员应当理解，在不脱离本发明实质的范围内，可以对本发明做一定的变形或修改；其制备方法也不限于实施例中所公开的内容。

Claims (8)

  1. 一种SCMA码本盲估计方法，其步骤为：
    1)使用因子图表示SCMA的资源映射情况，其中，将每一码本作为一变量节点，每一资源块作为一函数节点，码本和资源块之间包含连线表示该码本将占用与其连接的资源块发送数据；为每一码本设置一初始置信度向量；
    2)用户设备UE在LTE协议规定的固定时频资源块发送自己的数据之前，先发送一前导码信息；
    3)接收端根据指定时频资源块内收到的前导码信息和该因子图对码本使用情况进行估计，估计出每一码本是否有用户使用以及使用该码本的用户数。
  2. 如权利要求1所述的方法，其特征在于，步骤3)使用MPA算法的实现方法为：
    21)计算由变量节点发送到函数节点的消息，以及由变量节点发送到函数节点的消息；
    22)重复步骤21)直到达到约定的最高迭代次数；
    23)根据变量节点上收到的消息获得最终的取值概率值，选取概率值最大的用户数作为码本使用情况的估计结果。
  3. 权利要求2所述的方法，其特征在于，利用公式
    Figure PCTCN2017114303-appb-100001
    计算由变量节点发送到函数节点的消息
    Figure PCTCN2017114303-appb-100002
    其中，
    Figure PCTCN2017114303-appb-100003
    为第l次迭代时从其它连接到函数节点Fn的变量节点Vj发送到函数节点Fn的消息，
    Figure PCTCN2017114303-appb-100004
    代表函数节点Fn上接收到的符号Rn与每种情况所估计的数据矢量
    Figure PCTCN2017114303-appb-100005
    之间的欧几里德距离；Fn为发送到变量节点Vk的消息第n个函数节点。
  4. 如权利要求3所述的方法，其特征在于利用公式log(ea+eb)≈max(a,b)对
    Figure PCTCN2017114303-appb-100006
    进行化简，利用化简后得到的公式
    Figure PCTCN2017114303-appb-100007
    计算由变量节点发送到函数节点的消息。
  5. 如权利要求3所述的方法，其特征在于，利用公式
    Figure PCTCN2017114303-appb-100008
    计算由变量节点发送到函数节点的消息
    Figure PCTCN2017114303-appb-100009
    其中，
    Figure PCTCN2017114303-appb-100010
    为第l次迭代时从其它连接到变量节点Vk的函数节点Fj发送到变量节点Vk的消息。
  6. 如权利要求5所述的方法，其特征在于，利用公式log(ea+eb)≈max(a,b)对公式
    Figure PCTCN2017114303-appb-100011
    进行化简，利用化简后得到的公式
    Figure PCTCN2017114303-appb-100012
    计算由变量节点发送到函数节点的消息。
  7. 如权利要求1至6任一所述的方法，其特征在于，所述前导码信息的序列长度最短为一个符号的长度。
  8. 如权利要求7所述的方法，其特征在于，使用QPSK方案对发送的前导码进行调制，每一符号包含两个二进制位。
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