WO2010112506A1 - Procédé de détermination de métriques de distance pour des noeuds et de contre-hypothèses dans un algorithme de recherche arborescente, procédé de recherche arborescente et dispositif de détection pour la réalisation du procédé - Google Patents

Procédé de détermination de métriques de distance pour des noeuds et de contre-hypothèses dans un algorithme de recherche arborescente, procédé de recherche arborescente et dispositif de détection pour la réalisation du procédé Download PDF

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WO2010112506A1
WO2010112506A1 PCT/EP2010/054203 EP2010054203W WO2010112506A1 WO 2010112506 A1 WO2010112506 A1 WO 2010112506A1 EP 2010054203 W EP2010054203 W EP 2010054203W WO 2010112506 A1 WO2010112506 A1 WO 2010112506A1
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search
tree
nodes
constellation
distance metrics
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PCT/EP2010/054203
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German (de)
English (en)
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Björn Mennenga
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Technische Universität Dresden
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03178—Arrangements involving sequence estimation techniques
    • H04L25/03203—Trellis search techniques
    • H04L25/0321—Sorting arrangements therefor
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03178—Arrangements involving sequence estimation techniques
    • H04L25/03203—Trellis search techniques
    • H04L25/03229—Trellis search techniques with state-reduction using grouping of states
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03178—Arrangements involving sequence estimation techniques
    • H04L25/03203—Trellis search techniques
    • H04L25/03242—Methods involving sphere decoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L2025/03592—Adaptation methods
    • H04L2025/03598—Algorithms
    • H04L2025/03713—Subspace algorithms

Definitions

  • the invention relates to a method for determining distance metrics for nodes of a tree-searching algorithm and for determining counter-hypotheses for detecting multipoint-to-multipoint (MIMO) received signals in a telecommunication system.
  • the invention also relates to a method and a detector for detecting MIMO received signals.
  • MIMO systems multiple transmit and receive antennas
  • APP detector a posteriori probability
  • Tree search algorithms represent a very attractive option for achieving near-optimal performance in conjunction with multi-antenna systems with reasonable detection complexity.
  • Reliability values (so-called soft output values), as well as its implementation in an ASIC described.
  • the implementation for a 16-QAM (Quadrature Amplitude Modulation) system with 4 transmit and receive antennas each (4x4 MIMO) was chosen so that for each of the eight sequentially processed tree levels of the real replacement system, a hardware component is present, which together with an expansion unit for the determination of soft values by pipeline Processing can work in parallel. This allows the detector to receive at a data rate of up to 106 Mbit / s, but with greatly increased complexity due to the number of calculation units and with accuracy losses due to a greatly simplified soft-output determination.
  • the system model is introduced as complex-valued.
  • Vectors are highlighted by small bold symbols, matrices by large bold symbols.
  • a superscript T identifies the transpose of a vector or matrix, and a superscript H denotes the hermit (conjugate complex transpose) of a vector or matrix. £ denotes the set of complex numbers. With 9t the real-part education is marked and with 3 the imaginary-part-education.
  • the Euclidean norm, sign (-) returns the sign and sore (-) the value of each argument rounded to the nearest integer.
  • Fig. 1 the essential elements of the transmission path are shown. The description is in the baseband, so discrete.
  • the data is transmitted in blocks, pulse-shaping filters in the transmitter and receiver are not considered separately in the exemplary embodiment, but can be integrated into the channel model.
  • a vector u with independently and identically distributed data bits is encoded transmitter side with the outer channel code (1 14) the resulting stream of vectors c 'is bit-interleaved (1 16) and divided into blocks c. For transmission, the corresponding bits are displayed on symbols of complex constellation shown (1 18).
  • He £ NTXNR denotes the complex system matrix of size (N ⁇ xN R ), which is the
  • Transmission channel as well as transmit and receive side filters may contain and is known in the receiver for the detection.
  • a data block c (c (1), K, c (N ⁇ )) consists of N 7 , symbols with L bits each, which are optionally encoded and interleaved before transmission.
  • ß-QAM is made up of elements e ⁇ 1, ⁇ 3, K, + JQ-l ⁇
  • the transmitted data is estimated by means of a detector 122 shown in FIG. 1 and corresponding decoding 128 provided by the transmitter-side preprocessing, wherein the detection / decoding process can also be carried out iteratively.
  • 126 shows the interleaver used in the feedback.
  • the decoded data is then forwarded in a known manner via a hard-decision block 130 to a binary data receiver 132.
  • the object of the detector 122 considered herein, in which the present invention can be implemented, is to determine the bits c most likely to be sent and reliability information (L values) for these bits corresponding to the logarithmic ratio of whether one bit is a "1" or "0" or "+1” or "-1". These are taken from the received symbols, the
  • the search tree comprises several levels i, each of which represents a send symbol to be estimated.
  • Each of these possibilities is represented by a tree node and, by means of the interference caused by the corresponding symbol, leads to the remaining not yet estimated symbols
  • the estimation of the transmitted symbols x by means of the QR decomposition of the system matrix corresponds to the application of, for example, linear zero forcing (ZF, full interference suppression) or linear minimum mean square error (MMSE) with extended channel matrix and can analogously also for a real-valued substitute model, sorted QR decomposition (SQRD), or lattice-reduced system matrices.
  • ZF linear zero forcing
  • MMSE linear minimum mean square error
  • SQRD sorted QR decomposition
  • lattice-reduced system matrices the transmit points are transmitted by distortion to equivalent potential receive symbols in the I / Q plane, see Figure 3, and across all the antennas the most distorted transmit signal is searched for. This is done iteratively in the calculation of ⁇ (c, y, L ⁇ (c)) via the back-substitution of already estimated transmission symbols and the associated cumulative distance metrics ⁇ .
  • Tree search methods can be roughly divided into three classes of algorithms: depth search, metric-controlled search and breadth-first search, as described by J. Anderson and S. Mohan in "Sequential Coding Algorithms: A Survey and Cost Analysis", IEEE Transactions on Communications, Vol. 32, no. 2, p.169-176, February 1984.
  • the third class of tree search methods is formed by the so-called breadth-first search, such as the M algorithm or K-Best algorithm, as described by J. Anderson and S. Mohan in Sequential Coding Algorithms: A Survey and Cost Analysis, IEEE Transactions on Communications, Vol. 32, No.2, p.169-176, February 1984, and by S. Haykin, M. Sellathurai, Y. de Jong, and T. Willink in "Turbo-MIMO for Wireless Communications," IEEE Communications Magazine , Vol. 42, pp. 48-53, October 2004.
  • the possible successor nodes level i
  • the M or K best are selected for the calculations of the next level.
  • LSD List Sphere Detection
  • FIG. 5 A conventional processing flow for this algorithm is shown in Fig. 5 by way of a data flow diagram.
  • step 502 After initialization in step 502, the algorithm is performed as follows:
  • the interferences of the already estimated symbols must be removed from the receive symbol (step 504) and the distances to the child nodes determined (step 505).
  • the distances to the child nodes determined (step 505).
  • z For the selection of the cheapest Koten is z.
  • a calculation of all children's nodes and their sorting (step 506) are required according to their probabilities. 2.2 If the children's nodes of the current tree level (thus for the same already estimated parent node) have already been considered and sorted, then no further processing is required in this step and a parent node to be processed next is selected (step 508).
  • the search sphere may possibly be further limited.
  • One way of doing this is to store the candidates relevant to the search sphere and determine the search sphere via the sorting of the candidates (step 512), combined with a suitable sphere radius calculation (step 514).
  • Search sphere in this embodiment does not restrict further and the process goes to decision question 520 on.
  • the tree level i is increased by one (step 530).
  • the tree level is increased if all nodes in the current search plane which are within the search sphere have already been considered (decision YES at 520). Otherwise, the most likely not yet considered node is selected (step 522) and the tree level for that node is reduced (step 524). 5. The tree search is continued at 1..
  • the reliability information is calculated and stored from the determined leaf nodes (step 542).
  • the object of the invention is thus to reduce the complexity of the calculations for determining the distance metrics in tree search methods for the detection of MIMO received signals and thereby to increase the performance of the tree search for a signal detection, at the same time lower costs, both the hardware and the time required.
  • the process should be simple in its structure and implementation and allow a high flexibility of the search method with high performance at the same time.
  • a method for determining distance metrics for nodes a tree-searching algorithm and for determining counter-hypotheses for the detection of multipoint-to-multipoint (MIMO) received signals wherein the received signals are characterized by a modulation constellation.
  • the method includes the following steps: A geometric viewing surface defined by the modulation constellation is decomposed into a plurality of reference surfaces. Euclidean distances to a plurality of nearest constellation points of the modulation constellation of the received signals as representatives of the potential tree nodes are set for each of the reference surfaces, and the distances d or normalized distances r 2 d are stored in a look-up table. Upon receipt of a signal, a position estimate is made for it. In this case, the predefined reference surface is determined, in which the received signal is located. For this reference area, the predefined distances are retrieved from the lookup table and these are used in the tree search algorithm as an approximation of the distance metrics for the received signal.
  • the setting of the Euclidean distances can take place over a point representing the reference surface, and the determination of the reference surface then takes place by determining the reference surface in which the received signal or its representative lies.
  • the distance metrics can have significantly lower complexity compared to an exact calculation be approximated. Due to the associated algorithmic simplifications, the performance and area efficiency of the methods can be significantly increased.
  • symmetry of the transmit constellation can be advantageously exploited for relative position estimation.
  • the modulation constellation has a QAM
  • the constellation is that the geometric area of the viewing space is mapped to an eighth sector of a geometric area defined between four adjacent points of the QAM constellation.
  • the constellation point closest to the received signal is used as a reference point for the eighth sector, and the decomposed eighth sector is mapped onto the eighth sector of the QAM constellation in which the received signal is located.
  • data can be recycled for the method that has already been determined in a preceding step of the tree search algorithm, namely a step of the search order determination.
  • DE 10 2009 014 844.2 by the same Applicant describes a method for determining the search order of nodes in a tree search algorithm, in which the search order for the tree search is determined by a geometrical analysis of the relative position of a reception symbol with respect to constellation points of a lattice consisting of potential transmission symbols in the IQ plane of the QAM constellation, decision lines are defined by successive division of an eighth sector of a geometric area defined between four adjacent points of the QAM constellation by auxiliary straight lines for which search sequence sequences are predefined in order to approximate the search order.
  • the decision regions defined in this method can be reused as reference surfaces for approximation of the distance metrics.
  • the position Determining the search argument can be reused by an appropriate choice of the decision areas, the position estimation of other units, as used for example in the search order estimation.
  • the eighth sector is divided into isosceles-right triangles, and these are used as reference surfaces.
  • the method is scalable with regard to the accuracy of the approximated distance metrics and the calculation effort.
  • more accurate Euclidean distances to the potential nodes are known; however, more computational effort is required for the more accurate position determination.
  • the loss of search accuracy is negligible compared to an exact calculation of the distance metrics.
  • the area center or the square area center of the respective reference area may be advantageously defined.
  • MMSE detection minimum mean square error criterion
  • ⁇ 2 is the noise variance
  • a complexity-reduced search for counter-hypotheses can take place.
  • the counter-hypotheses can be determined directly.
  • for a leaf node as counter-hypothesis only grid points of the QAM constellation in purely real or imaginary direction are considered by the hypothesis. Due to the direct determinability of the counter-hypotheses and the known distances, a parallel observation of the leaves can take place.
  • the invention further includes a tree search method for detecting multipoint-to-multipoint received signals in a telecommunication system, comprising determining a search order for each receive symbol and determining distance metrics for each hypothesis and counter-hypothesis to the receive symbol, and wherein the distance metrics for each receive symbol be approximated according to one of the above-discussed embodiments of the method according to the invention for distance metric determination.
  • the method can be implemented and configured in such a way that the position of the reference surfaces is determined relative to defined grid points and by a suitable selection of nodes to be analyzed independently of the number of potential children nodes, as defined by the modulation that the complexity of the tree search method increases only slightly with the number of children's nodes due to extended sequences.
  • the distance approximation method can be designed in such a way that when using the method exclusively for determining the reliability information, the method has no influence on the search space limitation and thus on the search process, whereby even large approximations are virtually lossless for the tree search method.
  • the number of nodes to be examined can be significantly reduced since, after the position estimate of the received signal, the Euclidean distances to all potential nodes are determined.
  • invalid nodes can be determined directly; valid nodes are e.g. in gray-mapping in purely real and purely imaginary orientation of the leaf node with the lowest metric.
  • a parallel evaluation of the reliability information on all bits of the tree level is possible, which leads to a significantly simplified processing, especially in leaf nodes.
  • the number of intermediate states to be stored for example the states for restricting the search space or the intermediate results of the considered tree levels, can be significantly reduced by the Euclidean distances stored in sequences, which leads to simplifications in the hardware complexity. Due to the simplifications achieved, the tree-searching algorithm can be further simplified or parallelized, for example by the analysis of the sheet metrics and the resulting reliability information taking place independently of the actual tree search.
  • the tree search algorithm can be further simplified or parallelized, for example, by the analysis of the sheet metrics and the reliability information resulting therefrom being independent of the actual tree search.
  • the invention provides a detector arrangement for detecting multipoint-to-multipoint received signals in a telecommunication system comprising a plurality of modules for carrying out the above-described tree search method, one or more modules of which are provided for carrying out the inventive distance metric approximation method.
  • the reference surfaces for the position approximation of the received symbol and thus the approximation of the Euclidean distances can advantageously be chosen such that only cost-effective operations, as far as the hardware implementation is concerned, are required for the determination of the space in which the representative of the search argument lies.
  • the execution of the distance metric approximation in one or more modules is possible, which perform only rounding, sign-viewing, addition, subtraction and / or shift operations.
  • the approximation of the Euclidean distances can also be used for tree-search methods with a-priori information, provided that the a-priori information additively enters into the calculation of the distance metrics.
  • the determination according to the invention of the counter-hypotheses can be used loss-free for tree search methods with a priori information in the case of a choice of hypothesis adapted for the a-priori information.
  • the number of intermediate states to be stored in the tree search such as the
  • FIG. 1 shows a system model of a MIMO system with iterative detection
  • Fig. 22 shows a 64-QAM constellation with bit values for gray labeling
  • 5 is a data flow diagram of a conventional sphere detection
  • Fig. 7 illustrates the principle of geometric positioning for a 16-QAM
  • Figures 8a-f illustrate relative positioning with decision lines bounded by auxiliary straight lines
  • Fig. 9 illustrates a definition of reference points in a set of reference surfaces according to a first embodiment of the invention, in which the
  • FIG. 10 illustrates a definition of reference surfaces in a QAM constellation according to a second embodiment of the invention for determining the
  • FIG. 11 illustrates the Euclidean approximation according to the invention
  • FIG. 12 illustrates the counter-hypothesis determination according to the invention
  • Gray mapping and a 64-QAM shows the influence of the bias on the position determination in MMSE
  • FIG. 15 shows an embodiment variant of a list sphere detection algorithm according to the invention with approximation of the distances of the leaves;
  • Fig. 16 illustrates the performance of an implementation of the invention
  • FIG. 17 illustrates the complexity of an implementation of the invention
  • FIG. 18 illustrates the influence of a-priori information on the distance metric of the tree search methods
  • FIG. 19 illustrates the performance of an inventive implementation of an iterative detection / decoding process based on tree search detection with approximation of the distance metrics and inventive selection of counter-hypotheses in leaf nodes.
  • the plane-wise detection of the transmission signals takes place via the calculation of the
  • the distance metrics of the tree nodes result from the Euclidean distances between the representative of the search argument, the possibly adjusted interference reduced signal and the representatives of the potential transmit symbols in the form of the grid shown in Fig. 2, we have recognized that the distances can advantageously be derived by a geometric analysis.
  • Fig. 6 shows a 16-QAM and a 16-PSK constellation, which are each deposited according to the invention with a grid of reference surfaces.
  • the grid of reference surfaces is arranged arbitrarily to the constellation.
  • reference distances are set in advance and in a
  • Lookup table stored. This can be done via reference points of the reference surfaces. Upon receipt of a signal, the position within the raster is then determined for this, and for the raster sub-area in which the received signal is located, the predefined distances are retrieved from the lookup table and used in the tree search algorithm as an approximation of the distance metrics for the received signal.
  • the symmetry of a constellation can be advantageously exploited for relative position estimation.
  • the to y Determines the nearest constellation point by rounding and relative to the position estimate performed, ie the difference between constellation node and / considered ".
  • Quadrature Amplitude Modulation is sketched, wherein the grid is arranged such that the grid points coincide with constellation points of the transmission constellation.
  • the valid constellation points in the IQ plane are represented as filled black dots, and a favorable order of the first three tree nodes to be considered is indicated by the numbered 1 to 3 ringed numbers.
  • interference-reduced signal as well as the grid can be integrated.
  • Distances d and the normalized distances r u 2 d are calculated to all constellation points. This calculation of the distances takes place according to an advantageous embodiment before the tree search. If the position of y 1 "is analyzed with sufficient accuracy, the Euclidean distances to the nodes are also recognized and the distances can be expediently stored in a sequence in which each sequence element contains a distance to a possible constellation point
  • Constellation point is dependent. Due to the relative position, apart from the distances, the relative distance to the constellation points is also known. Thus, the point marked “1" in Fig. 7 is the nearest constellation point, and the point marked "2" is the one having the next greater Euclidean distance, etc. Thus, the Euclidean distances are stored in Euclidean distances a sequence with increasing distance appropriate. So y ⁇ "in another
  • the starting point for the approximation of the distances is thus the estimation of the relative position of the received signal and the determination of distances for the possible positions or decision areas.
  • the position determination can generally be arbitrary, with a sequence having corresponding distances to be created for each possible position or for each decision area in which the search argument can be located.
  • a method for QAM constellations can advantageously be used, as described in a simultaneously filed application entitled "Method for Determining the Search Order of Nodes in a Tree Search Algorithm . If a search order estimation is carried out in the course of a tree search method, then the results of this analysis can be reused for a distance approximation, for which it makes sense to use both methods However, the distance approximation can also be performed independently of a search sequence approximation.
  • the most favorable node is that node which is closest to the receive symbol y t ", which can be determined simply by rounding y ⁇ " onto the grid points, as in FIG. 8 (a) Rounding square is illustrated, which has a side length equal to the lattice spacing a and whose center coincides with that lattice point which is closest to the symbol and is denoted by 1 in FIG.
  • the first node of the search order is known.
  • the order of the further nodes to be examined can be predefined for all constellation points before the start of the tree search. With such an approximation, however, a relatively large error can result.
  • the more the position of the reception symbol deviates from the position of the constellation point the less accurate the search order thus approximated will be.
  • the greatest inaccuracy will result if the receive symbol is exactly at the intersection of the diagonal between four adjacent constellation points of the grid.
  • An improvement in the accuracy of the approximated search sequence is achieved by narrowing the area in which the reception symbol lies step by step over geometric comparisons, wherein ever smaller decision areas are defined as partial areas of the first decision area, namely the rounding square, each of these limited decision areas each represented by a reference point (pos').
  • This reference point may suitably be the area centroid, also the centroid of the area, the center of gravity of the Euclidean distances or the like of the respective one
  • Decision area are defined. For each of these decision areas or Reference points can be defined before starting the tree search another search order. The further the geometric analysis is continued before resorting to the predefined sequences for the continuation of the order, the smaller the decision area whose predefined sequences are used for the continuation of the search order, the better the position of the respective
  • Reference point (pos') coincide with the relative position of the receiving symbol and the more accurate the position estimate will be.
  • a first narrowing of the relative position of the receive symbol within the rounding square can be done by considering the sign sign (pos) of the real and imaginary part of the receive symbol.
  • the relative position (pos) of the receive symbol is limited to one of the quadrants of the rounding square, and thus the two potentially second-best nodes for the search order are given.
  • the method is applicable regardless of the number of potential children nodes, which is defined for example by the modulation, and the complexity of the algorithm increases only slightly with increasing constellation size.
  • Perpendicular on the connecting line between nodes 2 and 3 - the node 3 is the second least expensive and the node 2 the third least expensive, and for all relative positions below the angle bisector of the first quadrant, the node 2 is the second least expensive and the node 3 the third least expensive.
  • a node sequence with distances can be defined in advance.
  • the position determination is mapped to a relative position (pos) analysis, regardless of the actual size of the constellation.
  • the position estimate can be made with implementation-friendly operations, such as rounding, sign considerations, additions, subtractions, and / or shift operations.
  • the deviation of the distances resulting from (pos) and (pos') is negligibly small, so that the enumeration error of the predetermined distances is insignificant.
  • the center of the respective triangle (decision area) can be used in each case as a reference point (pos') for the further node enumeration.
  • the number of possible orders is limited. Due to the smaller number of considered nodes, the number of states to be stored in the tree search and, as a result, also the complexity of the conversions are reduced.
  • a received signal is rounded to a reference point which represents a subarea of an eighth sector of a geometric area defined between four adjacent constellation points of a QAM constellation.
  • the distance metrics for the received signal are approximated by the Euclidean distances of the reference point to nearest constellation points that have been calculated in advance and stored in a lookup table.
  • the eighth sector is shown divided into five decision areas as given after the approximation of a 7-node search sequence of Fig. 8 (e). These five decision areas are used here as reference areas for approximating the distance metrics.
  • a reception symbol y t "by a""mark. Is a reference point for each of the five reference surfaces. In the example shown these correspond to the centroid of the reference surfaces and in each case by a forms" x + "in.
  • Reference points the relevant reference for the distance approximation reference position is determined.
  • the Euclidean distances previously calculated for this reference position and stored in a lookup table are then used as an approximation of the distance metrics for the received signal in the tree search algorithm.
  • Fig. 10 shows an alternative advantageous embodiment of the decomposition according to the invention of an eighth sector of an area defined between four adjacent points of a QAM constellation into a plurality of reference areas for approximating the distance metrics for a received signal.
  • the eighth sector is divided into isosceles-right triangles, which are used as reference surfaces for the position estimation for distance approximation.
  • a reference point is also defined in each case for which Euclidean distances are calculated in advance and stored in order to be used as distance approximations for a received signal.
  • 9 and 10 differently divided eighth sectors may each be imaged by simple geometric operations such as shifting and / or mirroring to any eighth sector of a geometric surface defined between four adjacent points of a QAM constellation.
  • a relative position approximation is given for a receive symbol located at any position in the constellation, ie relative to the nearest constellation point.
  • Fig. 11 illustrates the Euclidean distances between the reference position of a receive symbol y t "and the nearest constellation points
  • Constellation points of a QAM constellation of which the lower left is the nearest constellation point, from which an eighth sector is divided into five reference areas as shown in Fig. 9.
  • the reception symbol y t is located in the reference area on the top left, whose reference point is marked by a" +.
  • the Euclidean distances between the reference point and the nearest constellation points are denoted by d u d 2 , d 3 and d 4 the Euclidean distances d u d 2 , d 3 and ⁇ approximate the distance metrics of the receive symbol to the respective grid constellation points.
  • the Euclidean distances are stored in sequences. This can be done in a fixed order, for example, in ascending order.
  • the approximated quadratic distances d to the representative of the search argument, ie the reception symbol are known.
  • mapping information results in further simplifications in the determination of the counter-hypotheses of the tree search.
  • This is illustrated in FIG. 12 using the example of a 64-QAM and Gray mapping.
  • the so-called gray mapping is used in an embodiment variant for the assignment of the bits to the transmitted symbols, the most favorable counterhypotheses, even if there is a priori information, can lie only on straight lines which run parallel to the coordinate axes (in FIG Image represented by bars) and intersect in the hypothesis node.
  • the selection of the nodes, as well as the approximation of the associated distance metrics, are thus reduced to the analysis of fewer elements, the distances being already determined by the determination of the hypothesis.
  • the efficiency and the impact on the tree search strongly depends on the design variant.
  • the method is also applicable to tree search methods with existing a-priori information.
  • another search criterion can be used in the tree search, as in the determination of the hypotheses or counter-hypotheses and their reliability information. If, for example, the MMSE method is used for the detection, the search results and the resulting reliability information are not reliable since they are subject to so-called bias. This is due to the noise suppression, the minimization of the mean square error.
  • MMSE detection may be performed as described by E. Zimmermann and G. Fettweis in “Unbiased MMSE Tree Search Detection for Multiple Antenna Systems", International Symposium on Wireless Personal Multimedia Communications (WPMC'06), September 2006
  • Fig. 13 illustrates the influence of the bias on the position determination.
  • Fig. 13-1 shows the reduction of the distances caused by the bias. The farther the constellation points are from the origin, the greater the reduction.
  • I- ⁇ can be determined. Since the determination of the hypotheses or counterhypotheses and their distance metrics for the calculation of the reliability information are independent of the actual search as well as the search space limitation, this method has in principle no influence on the search process. Improved identification of reliability information, however, improves search accuracy.
  • FIG. 5 A modified form of the list-sphere detection algorithm described in FIG. 5 is shown in FIG. This has a regularized data flow and has been described in detail in the previously filed DE patent application "Method for tree-based detection of received signals" of the same applicant.
  • the regularized algorithm is performed as follows:
  • the resulting interferences, as well as the interferences of the other already estimated symbols are removed from the receive symbol (step 1 104), and the distances to the Child nodes are detected (step 1 105) for selection the cheapest node is z.
  • Sort criterion is, for example, the reliability of the candidates.
  • the tree search is continued at 1.. 6.
  • the search is completed (decision 1 140), so z. For example, if the search tree has been completely traversed or termination conditions such as the maximum number of cycles are met, the reliability information is calculated and stored from the determined leaf nodes (step 1 142).
  • Elements of a ⁇ -QAM are evaluated by calculating the Euclidean distances and then further analyzed (see 1 105). The removal of the bias is done in parallel for all considered elements, i. in the leaf plane for Q elements at a ß -QAM. As a result, a relatively large number of elements have to be considered, of which only 1 + Vß (a hypothesis and / or counter hypotheses) for the calculation of the
  • FIG. 14 shows the algorithm modified according to the invention, wherein modules with the same or analogous function as in FIG. 13 are designated with analogous references, increased by 100.
  • the modified algorithm is characterized by an early approximation of the sorting and parallel consideration of follower nodes.
  • the correspondence of the search argument with the tree nodes is determined. For example, a method based on decision regions is used, analogous to the method presented above.
  • a method based on decision regions is used, analogous to the method presented above.
  • the subsequent sorting is omitted, and the possible sorting into a radius list is reduced to the sorting of a node into a list (1210).
  • one node per clock extension can be made.
  • the processes for distance estimation, for the determination of the valid nodes, for the parallel evaluation of the hypotheses and counterhypotheses can each be integrated in one processing module or can be carried out by means of several modules.
  • the results of other modules of the tree search, such as the search order, the approximation of the distances or the selection of valid or favorable nodes for the tree search or the determination of dissenting hypotheses or counter-hypotheses can be reused for the calculation of the reliability information.
  • FIG. 15 shows the performance and in FIG. 16 the complexity of the extended sphere detection algorithm with approximation of the distance metrics, with and without consideration.
  • the bias influence on the choice of hypotheses and counter-hypotheses compared to the traditional implementation. In detection without detector / decoder iterations, the performance is only slightly lower. If the influence of the bias is taken into account, the power loss of the tree search can be more than halved.
  • 17 illustrates the influence of the a-priori information on the distance metrics underlying the search order.
  • the distance metrics result from the metrics of the overlying levels, the Euclidean distance, and the a-priori information. Since these can be regarded as statistically independent according to the system model described, a quasi-random distribution over the distance of y t "results for the selected gray mapping.
  • the control of the accuracy of the iterations can also be done via the settings and adjustments of the search algorithm.
  • the influence of a priori The influence of the a-priori information can also be taken into account when determining the search order by determining the a-priori-specific order before or parallel to the tree search.
  • the consideration of the hypothesis or counterhypotheses and the inclusion of correction terms a use of the STA for the implementation is made possible.
  • the corresponding components of the algorithm can be integrated into one or more STA modules.
  • the control of the modules can then take place individually or via a VLIW. This provides the benefits of STA, high performance, low power consumption, easy expandability, and high flexibility to use for tree searching.
  • the invention can be used in connection with an OFDM (Orthogonal Frequency Division Multiplex) method as a digital transmission method, the system matrix H then includes the transmission channel in the frequency domain for one or more subcarriers.
  • OFDM Orthogonal Frequency Division Multiplex
  • the invention may also be used in conjunction with other multiple access methods, Direct Sequence CDMA (DS-CDMA) or Multi-Carrier CDMA (MC-CDMA) or Space Division Multiple Access (SDMA).
  • DS-CDMA Direct Sequence CDMA
  • MC-CDMA Multi-Carrier CDMA
  • SDMA Space Division Multiple Access
  • the invention is also applicable to a multi-user transmission method (MUT) to one or more receivers with collaborative detection of the received data.
  • the system matrix H then contains the transmission channels between the corresponding users. Multiple antennas may be used in the one or more receivers.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Radio Transmission System (AREA)

Abstract

Le procédé selon l'invention repose sur l'approximation heuristique de calculs de distance à réaliser lors de procédés de recherche arborescente, au moyen de métriques de distance prédéfinies, basées sur des domaines de décision, enregistrées dans des séquences. Les calculs de métriques de distance nécessaires, lors de la recherche, à l'analyse mathématique des noeuds à considérer dans des procédés de recherche arborescente, lors de laquelle la correspondance des noeuds arbre possibles avec l'argument de recherche est déterminée, sont transformés en une considération géométrique des noeuds à examiner. L'approximation de la position d'un représentant de l'argument de recherche à des points de référence de surfaces de référence et des distances euclidiennes prédéfinies aux noeuds arbre à considérer potentiellement, permet d'approcher les métriques de distance avec une complexité considérablement réduite. Les simplifications algorithmiques inhérentes permettent d'augmenter considérablement l'efficacité de rendement et de surface du procédé de recherche arborescente et d'un dispositif de détection mettant en oeuvre ce procédé.
PCT/EP2010/054203 2009-03-30 2010-03-30 Procédé de détermination de métriques de distance pour des noeuds et de contre-hypothèses dans un algorithme de recherche arborescente, procédé de recherche arborescente et dispositif de détection pour la réalisation du procédé WO2010112506A1 (fr)

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