CN114389757A - Sphere decoding detection method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a sphere decoding detection method and device, an electronic device and a storage medium, wherein the method comprises the following steps: carrying out QR decomposition on the channel matrix to obtain a received signal of each layer, and constructing an index of a hard decision constellation point lookup table according to the received signal; calculating Euclidean distances of constellation points of the corresponding layer; carrying out hard judgment constellation point search on all preset hard judgment search paths, and summing Euclidean distances of constellation points of all layers to obtain a hard judgment global optimal path; soft-decision constellation point search is carried out according to constellation points corresponding to the hard-decision global optimal path to obtain a soft-decision global optimal path, a soft-decision shortest Euclidean distance, an updated reversed polarity bit matrix and a shortest Euclidean distance, and LLR soft information corresponding to each bit of each layer is calculated according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix; the embodiment of the disclosure makes full use of the channel information and improves the decoding performance.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a sphere decoding detection method and apparatus, an electronic device, and a storage medium.
Background
In a commonly used Multiple Input Multiple Output (MIMO) detection algorithm, a Maximum Likelihood (ML) detection algorithm has the best performance, but the computation complexity, the modulation order and the number of transmitting antennas are in an exponential relationship, and the implementation on a hardware system is difficult in an actual system. Common detection algorithms with low computational complexity include a zero forcing algorithm and a minimum mean square error algorithm, which are simple to implement but have large performance loss. Therefore, the research focus of the MIMO detection algorithm is focused on: how to reduce the complexity of algorithm implementation and reach or approach the detection performance of the ML algorithm.
The computation complexity of the Sphere Decoding (SD) algorithm is much less than that of the ML detection algorithm, but the performance is closest to that of the ML detection algorithm, and the algorithm is an ideal signal detection algorithm. The detection principle of the sphere decoding algorithm is that the optimal signal point is found by searching all constellation point grids in a hypersphere with a certain radius. The standard sphere decoding detection algorithm has relatively high implementation complexity and is not beneficial to hardware implementation.
The embodiment of the related patent discloses a signal search sphere decoding algorithm of an MIMO receiver with fixed complexity, which traverses all constellation points under different modulation modes by using the results of Cholesky decomposition and zero forcing equalization to perform iterative search, calculates the accumulated Euclidean distance and then calculates the corresponding soft bit information. The patent needs to search all constellation points in a traversing way to obtain an optimal path, and the hardware realization recurrence degree is high.
The related patent embodiment discloses a spherical decoding detection method based on a super-large-scale integrated circuit, which comprises the steps of utilizing configured search nodes, starting from a first layer to perform table lookup sorting, reserving the node with the minimum Euclidean distance, then sequentially performing table lookup sorting on the other layers, reserving the search node with the minimum weight of the current layer, and outputting a decoding result when the last layer is searched. The result of the patent embodiment only contains the symbol information of the modulation result, and the utilization rate of the channel information is low.
The related patent embodiment also discloses a spherical decoding detection method based on depth-first search, wherein the upper limit of a total search node and the upper limits of search nodes in different layers need to be configured in the search process, then the search is carried out layer by layer according to the depth-first principle, the current search radius is updated to the weight of the searched bottommost node, and the decoding result is output until the number of the searched summary points is equal to the upper limit value or the search layer cannot continuously execute the search. The bottom layer search weight of the patent embodiment is continuously updated, the subsequent search range is updated accordingly, and the calculation amount during hardware implementation cannot be fixed.
Disclosure of Invention
The disclosed embodiments of the present disclosure provide a sphere decoding detection method and apparatus, an electronic device, and a storage medium, so as to improve decoding performance.
In order to achieve the above object, a first aspect of the embodiments of the present disclosure provides a sphere decoding detection method, including:
carrying out QR decomposition on the channel matrix to obtain a received signal of each layer, and constructing an index of a hard decision constellation point lookup table according to the received signal;
calculating the Euclidean distance of the constellation point of the corresponding layer according to the constellation point corresponding to the search node of each layer in the index of the hard-judgment constellation point lookup table;
carrying out hard judgment constellation point search on all preset hard judgment search paths, and summing Euclidean distances of constellation points of all layers under each hard judgment search path to obtain a hard judgment global optimum path, a hard judgment shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and according to the updated soft-judgment global optimal path, the soft-judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix are updated, and LLR soft information corresponding to each bit of each layer is calculated according to the updated soft-judgment global optimal path, the soft-judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
To achieve the above object, a second aspect of the embodiments of the present disclosure provides a sphere decoding detection apparatus, including:
the lookup table construction module is used for carrying out QR decomposition on the channel matrix to obtain a received signal of each layer and constructing an index of a hard decision constellation point lookup table according to the received signal;
the calculation module is used for calculating the Euclidean distance of the constellation points of the corresponding layer according to the constellation points corresponding to the search nodes of each layer in the index of the hard-judgment constellation point lookup table;
the hard judgment searching module is used for carrying out hard judgment constellation point searching on all preset hard judgment searching paths, and summing Euclidean distances of constellation points of all layers under each hard judgment searching path to obtain a hard judgment global optimum path, a hard judgment shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and the soft judgment searching module is used for searching soft judgment constellation points according to the constellation points corresponding to the hard judgment global optimal path, updating the hard judgment global optimal path to obtain a soft judgment global optimal path, updating the hard judgment shortest Euclidean distance to obtain a soft judgment shortest Euclidean distance, updating the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix, and calculating LLR soft information corresponding to each bit of each layer according to the updated soft judgment global optimal path, the soft judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
To achieve the above object, a third aspect of the embodiments of the present disclosure provides an electronic device, including:
a field programmable gate array FPGA or an integrated circuit ASIC;
a memory communicatively coupled to the FPGA or ASIC;
wherein the memory stores instructions executable by the FPGA or ASIC to enable the FPGA or ASIC to perform the method of the first aspect as described above.
To achieve the above object, a fourth aspect of the embodiments of the present disclosure provides a storage medium storing instructions executable by an FPGA or an ASIC, the instructions being executed by the FPGA or the ASIC to implement the method according to the first aspect.
The spherical decoding detection method and device, the electronic device and the storage medium provided by the embodiment of the disclosure construct an index of a hard-decision constellation point lookup table according to a received signal, calculate Euclidean distances of constellation points of corresponding layers according to constellation points corresponding to search nodes of each layer in the index of the hard-decision constellation point lookup table, search hard-decision constellation points of all preset hard-decision search paths, sum the Euclidean distances of the constellation points of all layers to obtain a hard-decision global optimum path, a hard-decision shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix, search soft-decision constellation points according to the constellation points corresponding to the hard-decision global optimum path, update the hard-decision global optimum path to obtain a soft-decision global optimum path, update the hard-decision Euclidean distance to obtain a soft-decision shortest Euclidean distance, and updating the reverse polarity bit matrix and the shortest Euclidean distance corresponding to the reverse polarity bit matrix, calculating LLR (log likelihood ratio) soft information corresponding to each bit of each layer according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reverse polarity bit matrix and the shortest Euclidean distance corresponding to the reverse polarity bit matrix, and further updating the hard-decision shortest Euclidean distance in a soft-decision constellation point by using the hard-decision global optimal path to obtain the soft-decision shortest Euclidean distance, updating the reverse polarity bit matrix and the shortest Euclidean distance updating the reverse polarity bit matrix, so that channel information is fully utilized, and the decoding performance is improved.
Drawings
Fig. 1 is a schematic diagram of a search tree of a sphere decoding detection method according to an embodiment of the present disclosure.
Fig. 2 is a flowchart of a sphere decoding detection method according to an embodiment of the disclosure.
Fig. 3 is a flowchart of step 201 in fig. 2.
Fig. 4 is a flowchart of step 202 in fig. 2.
Fig. 5 is a flowchart of step 203 in fig. 2.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
First, several terms referred to in the present application are resolved:
multiple Input Multiple Output (MIMO): the antenna diversity technology is an antenna system which uses a plurality of antennas at a transmitting end and a receiving end and forms a plurality of channels between transmitting and receiving; the core principle of the MIMO system is as follows: the spatial degrees of freedom provided by the multiple transmitting antennas and the multiple receiving antennas are utilized to improve the spectrum efficiency in wireless communication, so as to improve the transmission rate and improve the communication quality.
Maximum Likelihood (ML): the ML detection algorithm takes into account the effect of time dispersion on the received signal and uses the entire received signal to determine the most likely transmitted sequence.
Log likelihood Ratio (Log likelihood Ratio, LLR): are commonly used for soft decoding.
Sphere Decoding (SD): the SD algorithm is relatively close to the ML algorithm, the calculation complexity is smaller than that of the ML algorithm, and the complexity of the SD algorithm depends on the size of the search radius; the general principle of SD is: the method comprises the steps of presetting a ball with a received signal as a circle center in a received signal space, mapping the ball into an ellipsoid in a transmitted signal space, searching possible transmitted signal points in the ellipsoid, and contracting the preset ball by taking the distance between the mapped point of the signal point and the received signal as a radius once a transmitted signal is found, so that subsequent searching can be performed in a smaller atmosphere.
Field Programmable Gate Array (FPGA): as a semi-custom circuit in the field of Application Specific Integrated Circuits (ASICs).
Application Specific Integrated Circuit (ASIC): refers to an integrated circuit designed and manufactured according to the requirements of a specific user and the needs of a specific electronic system, and an ASIC design can be performed by using a CPLD (Complex Programming logic device) and an FPGA (field programmable logic array).
Quadrature Amplitude Modulation (QAM): it is a vector modulation, the input bits are first mapped (usually using gray code) onto a complex plane (constellation) to form complex modulation symbols, and then I, Q components of the symbols (corresponding to the real and imaginary parts of the complex plane, i.e. horizontal and vertical directions) are modulated by amplitude modulation onto two carriers (cos wt and sin wt) which are orthogonal to each other (time domain orthogonal), respectively. Thus doubling its spectral utilization compared to Amplitude Modulation (AM). QAM is a technique of amplitude and phase joint modulation, and uses the amplitude and phase of a carrier to transmit information bits, so that a higher frequency band utilization rate can be achieved under the condition of the same minimum distance, and currently, QAM reaches 1024-QAM (i.e., 1024 sampling points) to the maximum. The higher the number of samples, the higher its transmission efficiency, for example, a 16-QAM signal with 16 samples, each sample representing one vector state, 16-QAM with 16 states, one of the 16 states specified per 4-bit binary number, 16 combinations of carriers and phases specified in 16-QAM, 4 bits transmitted per symbol and period of 16-QAM.
Through the accumulated Euclidean distance sum of each path and the corresponding constellation point calculated in the hard judgment searching step, the constellation point corresponding to the minimum value of all the accumulated Euclidean distances is the global shortest path of the hard judgment algorithm, namely, the hard judgment global optimal path, and then the step 204 is executed by using the hard judgment global optimal path, namely, the hard judgment global optimal path is used for further updating the hard judgment shortest Euclidean distance in the soft judgment constellation points to obtain the soft judgment shortest Euclidean distance, updating the reversed polarity bit matrix and updating the shortest Euclidean distance of the reversed polarity bit matrix, so that the channel information is fully utilized, and the decoding performance is improved.
The application scenario of the embodiment of the disclosure is applied to an MIMO system, wherein a search tree is constructed to perform hard judgment and soft judgment, so that the search paths of constellation points in the whole process of hard judgment and soft judgment are controlled through the search tree, the sum of the accumulated euclidean distances of each path calculated in the step of hard judgment search and the corresponding constellation points are used, the constellation point corresponding to the minimum value of all the accumulated euclidean distances is the hard judgment global optimal path, and then the hard judgment shortest euclidean distance is further updated in the soft judgment constellation points by using the hard judgment global optimal path to obtain the soft judgment shortest euclidean distance, update the reverse polarity bit matrix, and update the shortest euclidean distance of the reverse polarity bit matrix, thereby fully utilizing the channel environment and improving the decoding performance. Referring to fig. 1, schematically, a search tree according to an embodiment of the present disclosure is provided, where a highest layer is an nth layer, a next highest layer is an N-1 th layer, the N-1 th layer includes two nodes, which are a node 1 and a node 2, respectively, and the node 1 and the node 2 of the N-1 th layer are child nodes of the nth layer; if N is 4, it is defined that all nodes at the highest layer 4 below are child nodes at the layer 4, and for example, the child nodes at the layer 4 include: nodes 1 and 2 of layer 3, nodes 3, 4 and 5 of layer 2, and all nodes of layer 1 (not shown).
The scheme provided by the embodiment of the present disclosure relates to a sphere decoding detection method and apparatus, an electronic device, and a storage medium, and is specifically described with reference to the following embodiments, which first describe the sphere decoding detection method in the embodiment of the present disclosure.
The sphere decoding detection method provided by the embodiment of the disclosure is applied to an MIMO system, which can be applied to a terminal, a server, or software running in the terminal or the server. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application or the like that implements a sphere decoding detection method, but is not limited to the above form.
In some embodiments, the sphere decoding detection method may be implemented by an FPGA; in other embodiments, the sphere decoding detection method may be implemented by an ASIC.
Fig. 2 is an alternative flowchart of a sphere decoding detection method provided in an embodiment of the present disclosure, and the method in fig. 2 may include, but is not limited to, steps 201 to 204.
and 204, carrying out soft-decision constellation point search according to constellation points corresponding to the hard-decision global optimal path, updating the hard-decision global optimal path to obtain a soft-decision global optimal path, updating the hard-decision shortest Euclidean distance to obtain a soft-decision shortest Euclidean distance, updating the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix, and calculating LLR soft information corresponding to each bit of each layer according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
In step 201 of some embodiments, by performing QR decomposition on the channel matrix corresponding to the original signals, the correlation between the original signals may be removed to obtain the received signal of each layer. Step 201 further comprises:
in some embodiments, referring to fig. 3, step 201 may include, but is not limited to including, steps 301 through 303.
301, performing QR decomposition on the channel matrix to obtain an upper triangular matrix and a receiving signal of each layer;
By equalizing the received signals in step 302, interference between the received signals can be reduced.
In step 301, QR decomposition is performed on a channel matrix H, H ═ Q · R, where Q is the caciquid matrix and QQ is the caciquid matrixHI, R is an upper triangular matrix, and R is expressed by the following formula:
received signal Z ═ QHY is represented as follows:
wherein Z isNIncludes only the signal component of the Nth layer, ZN-1Including the signal component of layer N and the signal component of layer N-1, … …, and so on, Z1Including all signal components from layer N to layer 1; sNConstellation points representing the Nth layer
When step 201 of the embodiment of the present disclosure is started, a preset hard decision start flag signal takes effect, and a hardware module of an FPGA or an ASIC prepares a hard decision flow.
In some embodiments, referring to fig. 4, step 202 may include, but is not limited to including, steps 401 through 402.
In some embodiments, referring to fig. 5, step 203 may include, but is not limited to including, steps 501 through 502.
and 502, acquiring all constellation points corresponding to the minimum value of the accumulated Euclidean distances, and taking the acquired constellation points as a hard-decision global optimal path.
In this embodiment, through the cumulative euclidean distance sum of each path calculated in the hard decision search step and the corresponding constellation point, the constellation point corresponding to the minimum value of all the cumulative euclidean distances is the global shortest path of the hard decision process, that is, the constellation point is used as the hard decision global optimal path, that is, the search path with the minimum cumulative euclidean distance is selected as the hard decision global optimal path.
In some embodiments, step 202 may further comprise:
sequentially searching the sub-nodes corresponding to different layers according to a preset search tree; therefore, the constellation points corresponding to the high layer can be continuously updated in an iterative manner;
and calculating and reserving the Euclidean distance of the constellation point corresponding to the current child node.
Specifically, the searching of the child nodes corresponding to different layers according to the preset search tree includes:
keeping the path from the highest layer to the ith layer unchanged, and searching all child nodes of the i-1 layer under the path;
jumping back to the ith layer;
wherein, the highest layer is N layers, N is a positive integer, and i is a positive integer greater than 2.
Illustratively, the highest layer is taken as the nth layer:
keeping a first path from the Nth layer to the 2 nd layer unchanged, searching all child nodes of the 1 st layer under the current first path until the searching is finished, and jumping back to the 2 nd layer;
keeping a second path from the Nth layer to the 3 rd layer unchanged, searching all child nodes of the 2 nd layer under the current second path until the searching is finished, and jumping back to the 3 rd layer;
repeating the steps until all the hard judgment search paths are traversed;
and generating a hard judgment end mark signal.
Through the sub-steps of step 202, until all the hard decision search paths specified by the search tree are traversed, the search table gives a hard decision end flag signal to the hardware system, and at this time, the constellation point corresponding to the hard decision global optimal path, the shortest euclidean distance corresponding to the hard decision global optimal path, and the shortest euclidean distance corresponding to the reversed polarity bit matrix are sent to the rear-end soft decision module. The hard judgment flow can be realized by a hard judgment module in the FPGA or the ASIC, and the soft judgment flow can be realized by a soft judgment module in the FPGA or the ASIC.
In step 401 of some embodiments, a constellation point look-up table corresponding to 256QAM (256-order quadrature amplitude modulation) is taken as an example for illustration, where 256QAM totally includes 256 constellation points, that is, 256QAM includes 256 constellation pointsSelecting constellation points with Euclidean distances within a fixed range as candidate constellation points for searching the constellation points of 256 constellation points to be detected, and arranging the candidate constellation points in a constellation point lookup table according to the sequence of the Euclidean distances from the candidate constellation points to be detected from near to far; as shown in the constellation point lookup table in table 1 below, further, in order to reduce the table specification of the constellation point lookup table, 256 constellation points to be detected of 256QAM may be transformed to one quadrant through quadrant transformation, that is, the 256 constellation points to be detected are reduced by three quarters and sorted for 64 constellation points to be detected, and the specification of the constellation point lookup table is also reduced by three quarters, that is, 64 constellation points to be detected (1, 2, 3,.. page.64, respectively) are recorded as Bi,nWhere I denotes the I < th >, n denotes the constellation point, Bi,1Indicating that the constellation point to be detected corresponding to the ith layer is 1, Bi,2Indicating that the constellation point to be detected corresponding to the ith layer is 2, Bi,3Represents that the constellation point to be detected corresponding to the ith layer is 3, Bi,64The constellation points to be detected corresponding to the ith layer are represented as 64, the number of the hard constellation points corresponding to each constellation point to be detected is N, and the number of the corresponding soft constellation points is M, that is, each constellation point to be detected has N hard constellation points which are closer to the Euclidean distance thereof and M soft constellation points which are closer to the Euclidean distance thereof.
| Constellation point to be detected | Hard decision of the number of constellation points | Soft decision constellation point number |
| Bi,1Corresponding constellation point 1 | N | M |
| Bi,2Corresponding constellation point 2 | N | M |
| Bi,3Corresponding constellation point 3 | N | M |
| …… | N | M |
| Bi,nCorresponding constellation point 64 | N | M |
TABLE 1
In some embodiments, the highest layer is the nth layer, and step 402 includes:
calculating Euclidean distance of a constellation point of the Nth layer according to the constellation point corresponding to the first search node of the Nth layer;
removing the signal component of the first search node of the Nth layer from the related signal of the Nth-1 layer, and taking the signal component as an index of a hard-decision constellation point table look-up of the Nth-1 layer to obtain a constellation point corresponding to the first search node of the Nth-1 layer;
and calculating Euclidean distance of the constellation points of the N-1 th layer according to the constellation points corresponding to the first search node of the N-1 th layer.
And repeating the steps until the constellation point corresponding to the first search node of the layer 1 is obtained according to the information of all the constellation points of the N-1 layer, and finishing the hard constellation point search of a complete path.
Illustratively, taking the search node 1 at the highest level as an example, the step 402 includes:
according to the constellation point B corresponding to the searching node 1 of the Nth layer of the highest layerN,1Calculate outEuclidean distance of the N layer of constellation points;
removing the signal component of the searching node 1 of the highest layer N from the related signal of the next highest layer N-1, using the signal component as the index of the hard-decision constellation point table look-up of the layer N-1, and obtaining the constellation point B corresponding to the searching node 1 of the layer N-1N-1,1；
Constellation point B corresponding to search node 1 of layer N-1N-1,1Calculating Euclidean distance of constellation points of the N-1 th layer;
and so on until obtaining the constellation point B corresponding to the search node 1 of the 1 st layer according to all the constellation point information of the N-1 layer1,1And then the hard decision constellation point search of a complete path is completed.
In step 203, all the preset hard judgment search paths refer to all the hard judgment search paths specified by the preset search tree; the method comprises the steps of constructing a search tree, searching all designated hard judgment search paths for hard judgment constellation points, summing Euclidean distances of the constellation points of all layers, calculating the sum of the accumulated Euclidean distances of all paths and the corresponding constellation points, and taking the constellation point corresponding to the minimum value of all the accumulated Euclidean distances as a hard judgment global optimal path.
In this embodiment, the constellation point corresponding to the minimum value of all the accumulated euclidean distances is the global shortest path of the hard decision algorithm, that is, the hard decision global optimal path, by the accumulated euclidean distance sum of each path calculated in the hard decision search step and the corresponding constellation point.
In step 203 of some embodiments, a constellation point and a shortest euclidean distance corresponding to the global optimal path may also be obtained. In step 203, a hard decision constellation point search is performed from the highest layer according to a depth-first principle. For example, starting from the nth layer of the highest layer, hard constellation point search is sequentially performed on the (N-1) th layer to the 1 st layer until constellation point search of a complete path is completed.
Through the accumulated Euclidean distance sum of each path and the corresponding constellation point calculated in the hard judgment searching step, the constellation point corresponding to the minimum value of all the accumulated Euclidean distances is the global shortest path of the hard judgment algorithm, namely, the hard judgment global optimal path, and then the step 204 is executed by using the hard judgment global optimal path, namely, the hard judgment global optimal path is used for further updating the hard judgment shortest Euclidean distance in the soft judgment constellation points to obtain the soft judgment shortest Euclidean distance, updating the reversed polarity bit matrix and updating the shortest Euclidean distance of the reversed polarity bit matrix, so that the channel information is fully utilized, and the decoding performance is improved.
In step 204, the nth layer of the highest layer does not participate in the soft decision process; and storing the shortest Euclidean distance corresponding to each maximum likelihood solution and the corresponding inverse bit matrix thereof, performing soft-decision constellation point search on the shortest Euclidean distance, and calculating LLR soft information corresponding to each bit of each layer.
In some embodiments, the next highest layer is layer N-1, and step 204 includes:
taking the constellation point corresponding to each layer of hard-decision global optimal path as a base address of a soft-decision search table;
searching constellation points corresponding to the first search node of each layer from the soft judgment search table according to the base address;
updating a hard-judgment global optimal path according to the constellation points to obtain a soft-judgment global optimal path, updating a hard-judgment shortest Euclidean distance to obtain a soft-judgment shortest Euclidean distance, and updating a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and calculating LLR soft information corresponding to each bit of each layer according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
Illustratively, the next highest layer is layer N-1, and step 204 includes:
the constellation points corresponding to the N-1 layer hard-decision global optimal path
As the base address of the soft decision search table;
according to the base address
Is made softJudging constellation points corresponding to the first search node 1 of the (N-1) th layer in the search table; processing all constellation points from the N-2 th layer to the 1 st layer according to the hard judgment process;
jumping back to the N-1 layer;
according to the base address
Searching constellation points corresponding to the second search node 2 of the (N-1) th layer from the soft judgment search table; processing all constellation points from the N-2 th layer to the 1 st layer according to the hard judgment process;
jumping back to the N-1 layer;
repeating the steps until the soft judgment constellation point search of all layers is completed;
calculating the shortest Euclidean distance according to the reverse polarity bit matrix corresponding to the soft-judged constellation point;
and calculating LLR soft information corresponding to each bit of each layer according to the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
In the embodiment of the disclosure, the N-1 layer hard judges the constellation points corresponding to the global optimal path
As the base address of the soft-decision table, the soft-decision constellation points corresponding to the layer N-1 soft-decision search node 1 are obtained by searching the soft-decision constellation point lookup table, the constellation points of the rest N-2 layers are subjected to normal hard-decision processing, the layer N-1 is jumped back after the hard-decision search is finished, and the soft-decision constellation points are utilized
And searching for a soft judgment constellation point corresponding to the 2 nd search node of the N-1 st layer, and so on until the search of all soft judgment search nodes of the N-1 st layer is completed. Through the soft-decision search of the corresponding layer, the optimal path of the constellation point of the layer can be updated, and meanwhile, the shortest path of the (N-1) th layer can also be updated (or the result of the hard-decision processing is still reserved).
In step 204, the nth layer of the highest layer does not participate in the soft decision processing, and directly determines the constellation point corresponding to the global optimal path through hard decision
As the shortest path of soft judgment, the soft judgment starts from the layer N-1, and the layer N-1 is used for hard judgment of the constellation points corresponding to the global optimal path
As the base address of the soft-decision table, obtaining the soft-decision constellation point corresponding to the first soft-decision search node 1 of the N-1 st layer by searching the soft-decision constellation point table, carrying out the hard-decision process from the constellation points of the N-2 nd layer to the rest N-2 th layer of the 1 st layer, jumping back to the N-1 st layer after the hard-decision process is finished, and utilizing the base address
And searching for the soft judgment constellation points corresponding to the second soft judgment searching node 2 of the N-1 layer, and so on until the searching of all the soft judgment searching nodes of the N-1 layer is completed. Through the soft judgment search of the corresponding layer, the optimal path of the constellation point of the layer can be obtained through updating, meanwhile, the shortest path from the N-1 st layer, the N-2 nd layer to the 1 st layer can also be updated, and the result of the hard judgment processing is stored. And then, repeating the process for the rest N-2 layers until the soft judgment searching process from the N-2 layer to the 1 st layer is completed. By repeating the above process until the soft decision search path specified by the search tree is completed, the soft decision search table will give a soft decision search end flag signal, and at this time, soft information LLR of each bit of each layer is output, and the soft information LLR can be represented as follows:
wherein L isj,bSoft information, lambda, representing the b-th bit of the j-th layerMLThe shortest euclidean distance is represented,
a bit indicating the (j, b) -th position corresponding to the shortest euclidean distance,
a bit representing the (j, b) th position in the reverse polarity bit matrix,
and (3) representing the minimum partial Euclidean distance corresponding to the bit at the (j, b) th position in the reversed polarity bit matrix.
In a practical application scenario, the search process of hard decision and soft decision in the sphere decoding detection method comprises the following steps:
the hard decision flag signal is in effect; specifically, when the process is started, the hard judgment flag signal takes effect, and the hard judgment module prepares a hard judgment search process;
according to the depth priority principle, starting to search for the constellation points which are hard to judge from the highest layer, and searching for the constellation points of a complete hard judgment path; specifically, starting to search for the hard decision constellation points from the Nth layer of the highest layer, and sequentially searching for the hard decision constellation points from the (N-1) th layer to the 1 st layer to complete constellation point search of a complete hard decision path;
keeping the path from the Nth layer to the 2 nd layer unchanged, searching all child nodes of the 1 st layer under the current path until the search is finished, and jumping back to the 2 nd layer; keeping the path from the Nth layer to the 3 rd layer unchanged, and searching all child nodes of the 2 nd layer under the current path until the search is finished; repeating the steps until the traversal of all the hard judgment paths is completed;
generating a hard judgment search ending mark signal;
the soft decision flag signal is in effect; specifically, the soft decision flag signal takes effect, and the software module prepares a soft decision search flow;
according to the depth-first principle, starting to search soft judgment search points from a first soft judgment search point of a next high layer, and searching soft judgment points of a first complete soft judgment path; specifically, from a first soft-decision search point of a second highest layer N-1, searching from the (N-2) th layer to the (1) th layer in sequence to complete the search of a first complete soft-decision path; wherein, the Nth layer of the highest layer does not participate in soft judgment, and the soft judgment is carried out from the Nth-1 layer of the next highest layer;
jumping back to a second soft-decision searching node of the (N-1) th layer of the next high layer to start soft-decision searching points, and searching soft-decision points of a second complete soft-decision path; specifically, from a second soft-decision search point of a second highest layer N-1, searching from the (N-2) th layer to the (1) th layer in sequence to complete the search of a second complete soft-decision path until the search of all soft-decision search nodes of the (N-1) th layer is completed;
repeating the two steps, and completing the N-2 layer and the N-3 layer in sequence until the soft judgment search point of the 1 st layer is searched;
generating a soft judgment end mark signal; namely, the soft-decision search process is finished, and a complete constellation point search process, namely a hard-decision search point search process and a soft-decision search point search process, is completed.
The spherical decoding detection method provided by the embodiment of the disclosure comprises the steps of constructing an index of a hard-decision constellation point lookup table according to a received signal, calculating Euclidean distances of constellation points of corresponding layers according to constellation points corresponding to search nodes of each layer in the index of the hard-decision constellation point lookup table, searching hard-decision constellation points of all preset hard-decision search paths, summing the Euclidean distances of the constellation points of all layers to obtain a hard-decision global optimum path, a hard-decision shortest Euclidean distance, a reverse polarity bit matrix and a reverse polarity bit matrix, searching soft-decision constellation points according to the constellation points corresponding to the hard-decision global optimum path, updating the hard-decision global optimum path to obtain a soft-decision global optimum path, updating the hard-decision shortest Euclidean distance to obtain a soft-decision shortest Euclidean distance, updating the reverse polarity bit matrix and the reverse polarity bit matrix corresponding shortest Euclidean distance, calculating LLR soft information corresponding to each bit of each layer according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix; and selecting a fixed number of constellation points (the number of each layer can be different) in the search of each layer, traversing all paths in sequence, and simultaneously obtaining the cumulative Euclidean distance of each path and the corresponding constellation point, wherein the constellation point corresponding to the minimum value of all the cumulative Euclidean distances is the hard-decision global optimal path. And further updating the hard-judgment shortest Euclidean distance in the candidate soft-judgment constellation points by using the optimal path obtained by hard judgment to obtain the soft-judgment shortest Euclidean distance, updating the reversed polarity bit matrix and updating the shortest Euclidean distance of the reversed polarity bit matrix, so that the channel information can be fully utilized and the decoding performance is improved.
The embodiment of the disclosure can switch the corresponding constellation point lookup table and the constellation point lookup table according to different modulation modes and the number of antenna layers, thereby realizing a spherical decoding algorithm through hardware with lower and fixed computational complexity. The embodiment of the disclosure can better improve the decoding performance of the sphere decoding, and the sphere decoding method combining hard judgment and soft judgment can effectively utilize channel information (such as channel environment and the like) and improve the decoding performance of the algorithm.
According to the method and the device, the search trees and the constellation point lookup tables of different constellation points are constructed according to different channel qualities (channel environments), online switching can be realized by a receiver according to the actual channel environment in the working process, namely, paths of the search trees of the constellation points and the constellation point lookup tables can be switched online, all the constellation point search paths are complemented to be the same as the number of the search nodes of the longest path by utilizing invalid paths, the decoding performance of the algorithm can be better ensured, the complexity of the algorithm in the hardware realization process is reduced, the calculation complexity of the algorithm is fixed, and therefore hardware is easier to realize. Compared with the physical receiver which applies more MMSE signal detection algorithms, the embodiment of the disclosure has better decoding performance. In addition, the embodiment of the disclosure is realized by using FPGA or ASIC hardware, so that the quantitative production of products can be realized, and the communication quality is improved.
The embodiment of the present disclosure further provides a sphere decoding detection apparatus, which can implement the sphere decoding detection method, and the apparatus includes:
the lookup table construction module is used for carrying out QR decomposition on the channel matrix to obtain a received signal of each layer and constructing an index of a hard decision constellation point lookup table according to the received signal;
the calculation module is used for calculating the Euclidean distance of the constellation points of the corresponding layer according to the constellation points corresponding to the search nodes of each layer in the index of the hard-judgment constellation point lookup table;
the hard judgment searching module is used for carrying out hard judgment constellation point searching on all preset hard judgment searching paths, summing Euclidean distances of constellation points of all layers and obtaining a hard judgment global optimal path, a hard judgment shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and the soft judgment searching module is used for searching soft judgment constellation points according to constellation points corresponding to the hard judgment global optimal path, updating the hard judgment global optimal path to obtain a soft judgment global optimal path, updating the hard judgment shortest Euclidean distance to obtain a soft judgment shortest Euclidean distance, updating the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix, and calculating LLR soft information corresponding to each bit of each layer according to the updated soft judgment global optimal path, the soft judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
An embodiment of the present disclosure further provides an electronic device, including:
a field programmable gate array FPGA or an integrated circuit ASIC;
a memory communicatively coupled to the FPGA or ASIC;
wherein the memory stores instructions executable by the FPGA or ASIC to enable the FPGA or ASIC to perform the aforementioned sphere decoding detection method.
The FPGA or the ASIC can comprise, but is not limited to comprising a hard decision module and a soft decision module, wherein the hard decision module comprises the lookup table constructing module, the calculating module and the hard decision searching module, and the soft decision module comprises the soft decision searching module.
The embodiment of the present disclosure also provides a storage medium, where the storage medium stores instructions executable by the FPGA or the ASIC, and the instructions are executed by the FPGA or the ASIC to implement the sphere decoding detection method as described above.
The spherical decoding detection method, the spherical decoding detection device, the electronic equipment and the storage medium provided by the embodiment of the disclosure construct an index of a hard-decision constellation point lookup table according to a received signal, calculate Euclidean distances of constellation points of corresponding layers according to constellation points corresponding to search nodes of each layer in the index of the hard-decision constellation point lookup table, search hard-decision constellation points of all preset hard-decision search paths, sum the Euclidean distances of the constellation points of all layers to obtain a hard-decision global optimum path and a shortest Euclidean distance corresponding to a reverse polarity bit matrix, search soft-decision constellation points according to the constellation points corresponding to the hard-decision global optimum path, update the hard-decision global optimum path to obtain a soft-decision global optimum path, update the hard-decision shortest Euclidean distance to obtain a soft-decision shortest Euclidean distance, update the shortest Euclidean distance corresponding to the reverse polarity bit matrix and the reverse polarity bit matrix, and according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix, LLR soft information corresponding to each bit of each layer is calculated, and the hard-decision shortest Euclidean distance is further updated in a soft-decision constellation point by using the hard-decision global optimal path to obtain the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance of the reversed polarity bit matrix, so that the channel information is fully utilized, and the decoding performance is improved.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
It will be appreciated by those skilled in the art that the sphere decoding detection methods illustrated in fig. 2-5 are not intended to be limiting of the embodiments of the present disclosure, and may include more or fewer steps than those illustrated, or some of the steps may be combined, or different steps may be included.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.
Claims (10)
1. A sphere decoding detection method is characterized by comprising the following steps:
carrying out QR decomposition on the channel matrix to obtain a received signal of each layer, and constructing an index of a hard decision constellation point lookup table according to the received signal;
calculating the Euclidean distance of the constellation point of the corresponding layer according to the constellation point corresponding to the search node of each layer in the index of the hard-judgment constellation point lookup table;
carrying out hard judgment constellation point search on all preset hard judgment search paths, and summing Euclidean distances of constellation points of all layers under each hard judgment search path to obtain a hard judgment global optimum path, a hard judgment shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and according to the updated soft-judgment global optimal path, the soft-judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix are updated, and LLR soft information corresponding to each bit of each layer is calculated according to the updated soft-judgment global optimal path, the soft-judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
2. The method of claim 1, wherein the step of performing hard decision constellation point search on all preset hard decision search paths and summing euclidean distances of constellation points of all layers to obtain a shortest euclidean distance corresponding to a hard decision global optimal path and a reversed polarity bit matrix comprises:
the Euclidean distances of the constellation points of all the layers are summed to obtain the sum of the Euclidean distances accumulated by the constellation points;
and acquiring the constellation points corresponding to the minimum value of all the accumulated Euclidean distances, and taking the acquired constellation points as the hard-decision global optimal path.
3. The method according to claim 1, wherein calculating the euclidean distance of the constellation point of the corresponding layer according to the constellation point corresponding to the search node of each layer in the index of the hard constellation point lookup table further comprises:
sequentially searching the sub-nodes corresponding to different layers according to a preset search tree;
and calculating and reserving the Euclidean distance of the constellation point corresponding to the current child node.
4. The method according to claim 3, wherein the sequentially searching for the child nodes corresponding to different layers according to the preset search tree comprises:
keeping the path from the highest layer to the ith layer unchanged, and searching all child nodes of the ith-1 layer under the path;
jumping back to the ith layer;
wherein, the highest layer is the Nth layer, N is a positive integer, and i is a positive integer greater than 2.
5. The method according to claim 1, wherein the highest layer is an nth layer, and the calculating the euclidean distance between the constellation points of the corresponding layer according to the candidate constellation points of each layer comprises:
calculating Euclidean distance of a constellation point of the Nth layer according to the constellation point corresponding to the first search node of the Nth layer;
removing the signal component of the first search node of the Nth layer from the related signal of the Nth-1 layer, and taking the signal component as an index of a hard-decision constellation point table look-up of the Nth-1 layer to obtain a constellation point corresponding to the first search node of the Nth-1 layer;
and calculating Euclidean distance of the constellation points of the N-1 th layer according to the constellation points corresponding to the first search node of the N-1 th layer.
6. The method according to any one of claims 1 to 5, wherein the performing QR decomposition on the channel matrix to obtain the received signal of each layer, and constructing an index of a hard decision constellation point lookup table according to the received signal comprises:
performing QR decomposition on the channel matrix to obtain an upper triangular matrix and the receiving signal of each layer;
carrying out equalization processing on the received signal;
and constructing an index of the hard-decision constellation point lookup table according to the signal component of the received signal after equalization processing to obtain the constellation point of each layer.
7. The method according to any one of claims 1 to 5, wherein the next higher layer is an N-1 layer, and the performing soft decision constellation point search according to the hard decision global optimal path and the shortest euclidean distance corresponding to the inverse polarity bit matrix to obtain LLR soft information corresponding to each bit of each layer comprises:
taking the constellation point corresponding to each layer of hard-decision global optimal path as a base address of a soft-decision search table;
searching a constellation point corresponding to the first search node of each layer from the soft judgment search table according to the base address;
updating the hard-judgment global optimal path according to the constellation point to obtain a soft-judgment global optimal path, updating the hard-judgment shortest Euclidean distance to obtain a soft-judgment shortest Euclidean distance, and updating the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and calculating LLR soft information corresponding to each bit of each layer according to the updated soft-decision global optimal path, the soft-decision shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
8. A sphere decoding detection device, comprising:
the lookup table construction module is used for carrying out QR decomposition on the channel matrix to obtain a received signal of each layer and constructing an index of a hard decision constellation point lookup table according to the received signal;
the calculation module is used for calculating the Euclidean distance of the constellation points of the corresponding layer according to the constellation points corresponding to the search nodes of each layer in the index of the hard-judgment constellation point lookup table;
the hard judgment searching module is used for carrying out hard judgment constellation point searching on all preset hard judgment searching paths, and summing Euclidean distances of constellation points of all layers under each hard judgment searching path to obtain a hard judgment global optimum path, a hard judgment shortest Euclidean distance, a reversed polarity bit matrix and a shortest Euclidean distance corresponding to the reversed polarity bit matrix;
and the soft judgment searching module is used for searching soft judgment constellation points according to the constellation points corresponding to the hard judgment global optimal path, updating the hard judgment global optimal path to obtain a soft judgment global optimal path, updating the hard judgment shortest Euclidean distance to obtain a soft judgment shortest Euclidean distance, updating the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix, and calculating LLR soft information corresponding to each bit of each layer according to the updated soft judgment global optimal path, the soft judgment shortest Euclidean distance, the reversed polarity bit matrix and the shortest Euclidean distance corresponding to the reversed polarity bit matrix.
9. An electronic device, comprising:
a field programmable gate array FPGA or an integrated circuit ASIC;
a memory communicatively coupled to the FPGA or ASIC;
wherein the memory stores instructions executable by the FPGA or ASIC to enable the FPGA or ASIC to perform the method of any one of claims 1 to 7.
10. A storage medium storing instructions executable by an FPGA or an ASIC to implement a method according to any of claims 1 to 7.
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