CN103326813A - Method and device for searching soft sphere decoding in single-tree mode - Google Patents

Method and device for searching soft sphere decoding in single-tree mode Download PDF

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CN103326813A
CN103326813A CN2012100815319A CN201210081531A CN103326813A CN 103326813 A CN103326813 A CN 103326813A CN 2012100815319 A CN2012100815319 A CN 2012100815319A CN 201210081531 A CN201210081531 A CN 201210081531A CN 103326813 A CN103326813 A CN 103326813A
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CN103326813B (en
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黄剑华
王乃博
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Chen core technology Co., Ltd.
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Leadcore Technology Co Ltd
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Abstract

The invention provides a method and device for searching soft sphere decoding in a single-tree mode. Comparison is carried out through the weight of a current node and the maximum weight of the obtained K routes, namely, a comparison level which can be definitely obtained is introduced, and the maximum weight of the comparison level is used as a single-tree node to conduct deletion. Therefore, under the premise that the performance loss can be received, node deletion is further carried out, the performance of searching the soft sphere decoding in the single-tree mode is ensured, and complexity is further reduced.

Description

Single tree search List Sphere Decoder method and device
Technical field
The invention belongs to the mobile communication technology field, relate to the input aspect, particularly a kind of single tree search List Sphere Decoder method and device.
Background technology
MIMO-OFDM system signal detection algorithm divides linear detection algorithm and non-linear detection algorithm two classes.Linear algorithm commonly used mainly comprises ZF (ZF) and least mean-square error (MMSE), and its complexity is minimum, but performance is the poorest.Non-linear detection algorithm commonly used mainly comprises maximum likelihood (ML), disturb to eliminate (IC), decompose based on QR detection algorithm and globular decoding (SD), wherein ML is the Optimum Detection in the MIMO-OFDM system, but complexity is the highest, is unfavorable for real-time processing; Disturb and eliminate and calculate by simplifying based on the detection algorithm that QR decomposes, the realization suboptimum detects effect, obtains better balance between performance and complexity, but relative Maximum Likelihood Detection, still have larger performance loss, and disturb elimination the phenomenon of error propagation can occur; The globular decoding detection algorithm is proposed by Fincke and Pohst the earliest, is used for research integer least square problem.Nearest globular decoding technical research shows: the prerequisite that it can significantly reduce in complexity (becoming polynomial relation with number of transmit antennas) is issued to the error performance that approaches or be equal to Maximum Likelihood Detection.Therefore in the MIMO-OFDM of reality system, the performance that general receiving terminal adopts the SD algorithm to approach ML is used simultaneously some and can be reduced the strategy of complexity in the SD algorithm.
Suppose that the MIMO-OFDM system model is y=Hs+n, wherein, y is N RThe reception vector of * 1 dimension, H is N R* N TThe channel matrix of dimension, s is N TThe transmission symbolic vector of * 1 dimension, n is that variance is σ 2White Gaussian noise.Here N R, N TRepresent respectively reception antenna number, number of transmit antennas.Then ML is whole
Figure BDA0000146630210000011
Seek optimal solution in the space, as shown in the formula:
s ^ ML = arg min s ∈ C N T | | y - Hs | | 2
Globular decoding has been quoted the thought of Maximum Likelihood Detection, but it is to seek all possible transmission symbolic vector at a center for receiving in the ball that vector y, radius are r, namely satisfies inequality:
||y-Hs|| 2≤r 2
Owing to be in the constraint radius r, rather than whole Seek in the space and send symbolic vector, thereby reduced amount of calculation.Here C represents the symbolic number that modulation constellation points comprises,
Figure BDA0000146630210000023
Representative sends the space of symbolic vector.
The tree search plan of globular decoding generally has depth-first, breadth First and tolerance preferential.For hard sphere shape decoding, all be in order to find as soon as possible the ML solution to reach the purpose that reduces complexity and memory space, three class search plans have corresponding corrective measure.To depth-first search, common are and dynamically dwindle initial radium, fix each maximum access node of father node lower floor, adopt Schnorr Euchner ordering, QR ordering etc.; To BFS, common are that fixing every layer of maximum reserve section are counted, Schnorr Euchner ordering, QR ordering etc.; To measuring preferentially, also can use the thought of ordering and maximum surviving path.But these thoughts can only guarantee firmly to declare the performance of BER, LTE system for using Turbo decoding if preferably BLER performance will be arranged, then needs to guarantee the alternative symbolic vector of some, to produce fully accurately soft information, namely need to carry out List Sphere Decoder.
List Sphere Decoder generally has following three kinds of schemes:
Tabulation sphere decoding LSD: this scheme is calculated soft bit LLR (b according to search listing (supposing that size is K) L, n) (send n bit of symbolic vector s l layer and be designated as b L, n, 1≤l≤N T, 1≤n≤N c, N cRepresent number of bits corresponding to single transmission symbol, relevant with modulation system), but when tabulation is imperfect, some bit value can occurs and be empty phenomenon, choose a quite larger radius and produce fully accurately soft information, this will cause very high complexity; Compose into maximum, this will cause soft information inaccurate; Weights or current search radius approximate evaluation bit value according to symbolic vector in the tabulation are empty weights.
The hard sphere shape decoding SoD-SDA (some paper also is called RTS:Repeated TreeSearch) of soft-decision: this scheme is done first a hard sphere shape decoding, finds as early as possible a maximum likelihood solution s ML, corresponding bit is b ML, then for each
Figure BDA0000146630210000031
(it is right to represent
Figure BDA0000146630210000032
Negate), try again hard sphere shape decoding.The soft information that obtains like this is fully accurately, but without being suspected to have very high complexity.
Single tree search STS (Single Tree Search): this scheme is to calculate the bit weights while searching for, and is specially and finds first a solution, is initialized as ML and separates.In each search afterwards, for non-leaf node, separate if can not upgrade ML, or current ML separates the node of opposite bit weights, abandon this node and the branch below it; Otherwise access this node.For leaf node, if its weights are less than the weights that current ML separates, then upgrade ML and separate; If otherwise weights are separated the weights of opposite bit less than current ML, also will be updated to current weight.Advantage is to calculate the bit weights while searching for, and can save part storage, if shortcoming is all bit values not to be sky, needs larger initial radium, and the worst situation is to search for complete tree.
The advantage of the relative RTS of STS just can find maximum likelihood solution s by once searching for exactly MLWith each bit of each layer
Figure BDA0000146630210000033
The advantage of the relative LSD of STS is in the identical initial radium, if will reach identical decoding performance, the nodes of STS access than LSD still less, and the memory space that needs is also little.
Describe for convenient, remember that current maximum likelihood solution is s ML, weights are λ ML, corresponding bit is
Figure BDA0000146630210000034
Figure BDA0000146630210000035
Expression is to s MLN bit negate of l layer, the minimum weights of its correspondence are
Figure BDA0000146630210000036
s kBe the path of current accessed node to root, corresponding weights are
Figure BDA0000146630210000037
b L, nBe s kCorresponding n bit value of l layer.According to the λ that obtains MLAnd
Figure BDA0000146630210000038
Just can calculate soft information by following formula:
LLR ( b l , n ) = 1 σ 2 ( λ ML - λ l , n ML ‾ ) , b l , n = 1 1 σ 2 ( λ l , n ML ‾ - λ ML ) , b l , n = 0
The concrete search procedure of existing STS is as follows:
1) at first ML is separated s MLBe initialized as sky, weights λ ML=+∞ is corresponding with each bit of each layer
Figure BDA00001466302100000310
Also be initialized as+∞;
2) search for, (weights are to present node ), if k=1 goes to 3); Otherwise go to 4)
3) if current sign vector s 1Weights
Figure BDA0000146630210000041
Show that can upgrade ML separates.Then need following renewal:
Right
Figure BDA0000146630210000042
Figure BDA0000146630210000043
Value is updated to λ ML
Then s ML=s 1,
Figure BDA0000146630210000044
If current sign vector s 1Weights
Figure BDA0000146630210000045
Separate although then can not upgrade ML, need do as judging:
If right
Figure BDA0000146630210000046
Have
Figure BDA0000146630210000047
Then upgrade
Figure BDA0000146630210000048
Get back to 2);
4) need this moment judgement to want to abandon present node and its subtree, please refer to Fig. 1, it is existing STS knot removal rule schematic diagram, as shown in Figure 1,
If
Figure BDA0000146630210000049
More than or equal to the maximum among the set Ψ
Figure BDA00001466302100000410
Then explanation search present node and subtree thereof can not be upgraded s MLCan not upgrade
Figure BDA00001466302100000411
Therefore get back to 2 after abandoning this node and subtree thereof); Otherwise need the access present node.Wherein, &Psi; = { &lambda; l , n ML &OverBar; | ( l &GreaterEqual; k , n = 1,2 , . . . , N C ) I ( b l , n = b l , n ML &OverBar; ) } U { &lambda; l , n ML &OverBar; | ( l < k , n = 1,2 , . . . , N C ) } .
Although STS has the node of oneself to delete rule, send out under the condition of receipts at the many antennas of high order modulation, need the nodes of access still very large, complexity is still very high.The way that reduces at present complexity is to carry out node by the maximum of setting soft bit to delete, this maximum is established to such an extent that too smallly then can not satisfy performance requirement, establishes the complexity that then substantially do not reduce too much.How under the prerequisite of guaranteed performance, deleting as much as possible node, is the problem that STS must solve.
Summary of the invention
The object of the present invention is to provide a kind of single tree search List Sphere Decoder method and device, thereby under guaranteed performance loss acceptable prerequisite, further reduce complexity.
For solving the problems of the technologies described above, the invention provides a kind of single tree search List Sphere Decoder method, comprising:
Search for, obtain the K paths, obtain the maximum weights in this K paths
The residue node is continued search, with weights and the maximum weights of present node
Figure BDA0000146630210000052
Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure BDA0000146630210000053
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights Then access present node.
Optionally, in described single tree search List Sphere Decoder method, when the weights of this present node more than or equal to these maximum weights
Figure BDA0000146630210000055
And this present node is positioned at N TDuring layer, finish search.
Optionally, in described single tree search List Sphere Decoder method, when the weights of this present node less than these maximum weights And when present node is positioned at the 1st layer, upgrade described maximum weights
Figure BDA0000146630210000057
And the path at present node place is replaced
Figure BDA0000146630210000058
The path, place forms new K paths.
Optionally, in described single tree search List Sphere Decoder method, the maximum weights after the renewal
Figure BDA0000146630210000059
Value for weights maximum in the new K paths.
Optionally, in described single tree search List Sphere Decoder method, after search finishes, be not updated if exist
Figure BDA00001466302100000510
Then use greater than maximum weights
Figure BDA00001466302100000511
In a right value update of minimum.
Optionally, in described single tree search List Sphere Decoder method, described K value is chosen according to the input simulation result.
Optionally, in described single tree search List Sphere Decoder method, before obtaining the K paths, utilize
Figure BDA00001466302100000512
Carrying out node deletes.
The present invention also provides a kind of single tree search List Sphere Decoder device, comprising:
The first search module in order to search for, obtains the K paths, obtains the maximum weights in this K paths
Figure BDA00001466302100000513
The second search module is in order to the residue node is continued search, with weights and the maximum weights of present node Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure BDA00001466302100000515
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure BDA00001466302100000516
Then access present node.
Optionally, in described single tree search List Sphere Decoder device, described the second search module also proceeds as follows:
When the weights of this present node more than or equal to these maximum weights
Figure BDA0000146630210000061
And this present node is positioned at N TDuring layer, finish search.
Optionally, in described single tree search List Sphere Decoder device, described the second search module also proceeds as follows:
When the weights of this present node less than these maximum weights And when present node is positioned at the 1st layer, upgrade described maximum weights
Figure BDA0000146630210000063
And the path at present node place is replaced
Figure BDA0000146630210000064
The path, place forms new K paths.
Optionally, in described single tree search List Sphere Decoder device, the maximum weights after the renewal
Figure BDA0000146630210000065
Value for weights maximum in the new K paths.
Optionally, in described single tree search List Sphere Decoder device, described the second search module also proceeds as follows:
After search finishes, be not updated if exist
Figure BDA0000146630210000066
Then use greater than maximum weights
Figure BDA0000146630210000067
In a right value update of minimum.
Optionally, in described single tree search List Sphere Decoder device, described K value is chosen according to the input simulation result.
Optionally, in described single tree search List Sphere Decoder device, also comprise correcting module, after finishing in order to search, be not updated if exist
Figure BDA0000146630210000068
Then use greater than maximum weights
Figure BDA0000146630210000069
In a right value update of minimum.
In single tree search List Sphere Decoder method provided by the invention and device, the maximum weights in the weights by present node and the K paths that obtains
Figure BDA00001466302100000610
Compare, namely introduce one and can determine the maximum weights that obtain The benchmark of deleting as single tree node, thus under guaranteed performance loss acceptable prerequisite, further carry out node and delete, namely guaranteed the performance of single tree search List Sphere Decoder, further reduced complexity.
Description of drawings
Fig. 1 is existing STS knot removal rule schematic diagram;
Fig. 2 is the schematic flow sheet of single tree search List Sphere Decoder method of the embodiment of the invention;
Fig. 3 is the module diagram of single tree search List Sphere Decoder device of the embodiment of the invention.
Fig. 4 is single tree schematic diagram of three layers.
Embodiment
Below in conjunction with the drawings and specific embodiments single tree search List Sphere Decoder method provided by the invention and device are described in further detail.According to the following describes and claims, advantages and features of the invention will be clearer.It should be noted that, accompanying drawing all adopts very the form of simplifying, only in order to convenient, the purpose of the aid illustration embodiment of the invention lucidly.
Core concept of the present invention is, a kind of single tree search List Sphere Decoder method and device are provided, the maximum weights in the weights by present node and the K paths that obtains
Figure BDA0000146630210000071
Compare, namely introduce one and can determine the maximum weights that obtain
Figure BDA0000146630210000072
The benchmark of deleting as single tree node, thus avoided whole single tree search in the existing method all pass through the maximum of the soft bit of setting
Figure BDA0000146630210000073
Carry out node and delete, be subject to this Value, can not satisfy simultaneously guaranteed performance and reduce the problem of complexity, improved the reliability that single tree node is deleted, namely guaranteed the performance of single tree search List Sphere Decoder, reduced complexity.
Please refer to Fig. 2, it is the schematic flow sheet of single tree search List Sphere Decoder method of the embodiment of the invention.As shown in Figure 2, described single tree search List Sphere Decoder method comprises:
S10: search for, obtain the K paths, obtain the maximum weights in this K paths
Figure BDA0000146630210000075
S11: the residue node is continued search, with weights and the maximum weights of present node
Figure BDA0000146630210000076
Relatively, if the weights of this present node are more than or equal to these maximum weights Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure BDA0000146630210000078
Then access present node.
Accordingly, the present embodiment also provides a kind of single tree search List Sphere Decoder device, please refer to Fig. 3, and it is the module diagram of single tree search List Sphere Decoder device of the embodiment of the invention.As shown in Figure 3, described single tree search List Sphere Decoder device comprises:
The first search module 20 in order to search for, obtains the K paths, obtains the maximum weights in this K paths
Figure BDA0000146630210000081
The second search module 21 is in order to the residue node is continued search, with weights and the maximum weights of present node
Figure BDA0000146630210000082
Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure BDA0000146630210000083
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure BDA0000146630210000084
Then access present node.
Concrete, in single tree search List Sphere Decoder method and device that the present embodiment provides, comprise mainly that for the deletion of node two stages carry out:
At first be the phase I, utilize the first search module 20 execution in step S10: search for, obtain the K paths, obtain the maximum weights in this K paths
Figure BDA0000146630210000085
At this, described K paths namely comprises K leaf node.The searching method of described K paths can utilize existing techniques in realizing, namely as described in the background art: at first ML is separated s MLBe initialized as sky, initial radium is made as+∞, i.e. weights λ ML=+∞ is corresponding with each bit of each layer
Figure BDA0000146630210000086
Also be initialized as+∞; Then, search for, (weights are to present node
Figure BDA0000146630210000087
), if k=1 then advances s ML, λ MLAnd
Figure BDA0000146630210000088
Renewal, otherwise, then enter and judge whether to carry out deleting of node.When carrying out deleting of node in this phase I, can utilize in the prior art more than or equal to the maximum among the set Ψ
Figure BDA0000146630210000089
Carry out node and delete, wherein, &Psi; = { &lambda; l , n ML &OverBar; | ( l &GreaterEqual; k , n = 1,2 , . . . , N C ) I ( b l , n = b l , n ML &OverBar; ) } U { &lambda; l , n ML &OverBar; | ( l < k , n = 1,2 , . . . , N C ) } . Described K value can be chosen according to the input simulation result, concrete, get different numerical value for K and carry out certain emulation, for example, carry out emulation for K value 3～30, consider the performance of single tree search List Sphere Decoder, in the receivable scope of performance, choose the K value, Yi Zhi, the K value is less, and then complexity is less, and the performance of single tree search List Sphere Decoder will be affected simultaneously; And the K value is larger, and then complexity is larger, but the performance of single tree search List Sphere Decoder can be protected.Can choose different K values according to the simulation result of different modulation systems, the application does not do restriction to the concrete value of K.
Maximum weights in obtaining K paths and this K paths
Figure BDA00001466302100000811
Afterwards, carry out second stage, utilize the second search module 21 execution in step S11, at this, because when present node is leaf node, carries out node and delete and have little significance, therefore preferred, carrying out node when deleting, for the 2nd to N TThe node of layer, concrete:
If present node is not positioned at the 1st layer:
The residue node is continued search, with weights and the maximum weights of present node Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure BDA0000146630210000092
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure BDA0000146630210000093
Then access present node.
At this, adopt the depth-first STS of SE ordering, for the weights of present node more than or equal to these maximum weights
Figure BDA0000146630210000094
Situation, abandon the subtree of present node and it, specifically comprise the following processing:
If this present node is in N TDuring layer, finish search, namely except abandoning present node and its subtree, also abandoned other node of not searching for;
If this present node is in N TDuring arbitrary one deck in-1 layer to the 2nd layer, then abandon present node and its subtree, at this moment, if need the node of search without residue, then finish search, otherwise then search for next node, be specially the next brother's node subsequent node of present node same layer (namely with) of present node.
For the weights of present node less than these maximum weights
Figure BDA0000146630210000095
Situation, the access present node, specifically comprise the following processing:
If this present node is in N TDuring arbitrary one deck in the 2nd layer of layer, the access present node namely obtains the information of this node so that continue to walk downward, and namely continues the subtree under this node of search;
If present node is positioned at the 1st layer: according to circumstances upgrade possibly s ML, λ MLAnd
Figure BDA0000146630210000096
Also upgrade possibly
Figure BDA0000146630210000097
S wherein ML, λ MLAnd
Figure BDA0000146630210000098
Update method identical with existing method,
Figure BDA0000146630210000099
Update method as follows:
If its routine weight value
Figure BDA00001466302100000910
Then current path is replaced
Figure BDA00001466302100000911
The path, place forms new K paths, remembers that the maximum of new K paths weights is
Figure BDA00001466302100000912
Otherwise do not upgrade.
When if this present node is in the 1st layer, the weights of present node are less than these maximum weights at this moment Namely explanation at least can be right
Figure BDA0000146630210000101
Upgrade, at this moment,
Figure BDA0000146630210000102
May be updated to the weights in path, present node place
Figure BDA0000146630210000103
Also might be updated to second largest weights in the K paths, at this, only needing to do a comparison step can obtain, and in a word, at this moment, it is right to need Upgrade, after obtaining upgrading
Figure BDA0000146630210000105
Simultaneously, originally
Figure BDA0000146630210000106
After a paths in the corresponding K paths also will be updated
Figure BDA0000146630210000107
Corresponding path substitutes.At this, if should
Figure BDA0000146630210000108
For a value of weights minimum in the K paths, then also will upgrade λ ML, namely occur
Figure BDA0000146630210000109
Operation, in addition, right
Figure BDA00001466302100001010
Figure BDA00001466302100001011
Value is updated to this
Figure BDA00001466302100001012
Namely this moment renewal process will occur, which specifically need to be done upgrade, identical with considering of prior art.It should be noted that, the application pays close attention to is as far as possible not in the situation of performance, and more, deletion of node more easily that is to say, about s ML, λ MLAnd
Figure BDA00001466302100001013
Deng the renewal of each several part content, can be corresponding to prior art.
Carry out the node of Dan Shu according to above-mentioned single tree search List Sphere Decoder method and device and delete, just can improve the reliability that single tree node is deleted, namely guaranteed the performance of single tree search List Sphere Decoder, reduced complexity.
Further, for the reliability of bonding tree search List Sphere Decoder, the application also is not updated existence Namely
Figure BDA00001466302100001015
For initial value (usually be set as+∞) situation given considering.After search finished, existence was not updated
Figure BDA00001466302100001016
Then use greater than maximum weights
Figure BDA00001466302100001017
In a right value update of minimum.At this moment, search finishes, according to above-mentioned search, can determine that weights in the K paths are K minimum weights, and if appearance is not updated
Figure BDA00001466302100001018
Situation, then then upgraded with the weights of K+1 minimum.
Can more delete node in order to further specify single tree search List Sphere Decoder method and the device that the application provides, subsequently will lift an example that comprises concrete numerical value, with clearer, distinct explanation the application's advantage.Please refer to Fig. 4, it is single tree schematic diagram of three layers.When utilizing existing method to search for this Dan Shu, at initial radium (r 2When 〉=1.8) searching in the scope, need complete tree of access, namely can not reduce complexity, the result who obtains at last is: s ML = 0,0 0,0 0,1 , λ ML＝0.7 &lambda; l , n ML &OverBar; = 1.7,1.3 1.3,0.8 0.9,0.9 .
And when utilizing single tree search List Sphere Decoder method of the present embodiment and device to conduct interviews, get K=3, then search node v 7Will finish search, obtain at last s ML = 0,0 0,0 0,1 , λ ML＝0.7
Figure BDA0000146630210000112
&lambda; l , n ML &OverBar; = 1.7,1.3 1.3,0.8 0.9,0.9 , Obtaining simultaneously K+1 minimum weights is 1.3.At this, during owing to the end search,
Figure BDA0000146630210000114
Be not updated, need thus by K+1 approximate the obtaining of minimum weights, thereby can affect the accuracy of this bit soft information, but owing to found
Figure BDA0000146630210000115
In the individual alternative symbolic vector
Figure BDA0000146630210000116
Minimum K, therefore very little on the decoding performance impact, in tolerance interval.But do not need access node v 14, v 3, v 8, v 15, v 9, v 16, greatly reduced complexity.
Foregoing description only is the description to preferred embodiment of the present invention, is not any restriction to the scope of the invention, and any change, modification that the those of ordinary skill in field of the present invention is done according to above-mentioned disclosure all belong to the protection range of claims.

Claims (14)

1. a single tree search List Sphere Decoder method is characterized in that, comprising:
Search for, obtain the K paths, obtain the maximum weights in this K paths
Figure FDA0000146630200000011
The residue node is continued search, with weights and the maximum weights of present node
Figure FDA0000146630200000012
Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure FDA0000146630200000013
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure FDA0000146630200000014
Then access present node.
2. single tree search List Sphere Decoder method as claimed in claim 1 is characterized in that, when the weights of this present node more than or equal to these maximum weights And this present node is positioned at N TDuring layer, finish search.
3. single tree search List Sphere Decoder method as claimed in claim 1 is characterized in that, when the weights of this present node less than these maximum weights
Figure FDA0000146630200000016
And when present node is positioned at the 1st layer, upgrade described maximum weights And the path at present node place is replaced The path, place forms new K paths.
4. single tree search List Sphere Decoder method as claimed in claim 3 is characterized in that the maximum weights after the renewal
Figure FDA0000146630200000019
Value for weights maximum in the new K paths.
5. single tree search List Sphere Decoder method as claimed in claim 3 is characterized in that, after search finishes, is not updated if exist
Figure FDA00001466302000000110
Then use greater than maximum weights
Figure FDA00001466302000000111
In a right value update of minimum.
6. single tree search List Sphere Decoder method as claimed in claim 1 is characterized in that, described K value is chosen according to the input simulation result.
7. such as each the described single tree search List Sphere Decoder method in the claim 1 to 6, it is characterized in that, before obtaining the K paths, utilize
Figure FDA00001466302000000112
Carrying out node deletes.
8. a single tree search List Sphere Decoder device is characterized in that, comprising:
The first search module in order to search for, obtains the K paths, obtains the maximum weights in this K paths
Figure FDA00001466302000000113
The second search module is in order to the residue node is continued search, with weights and the maximum weights of present node
Figure FDA0000146630200000021
Relatively, if the weights of this present node are more than or equal to these maximum weights
Figure FDA0000146630200000022
Then abandon present node and its subtree; If the weights of this present node are less than these maximum weights
Figure FDA0000146630200000023
Then access present node.
9. single tree search List Sphere Decoder device as claimed in claim 8 is characterized in that, described the second search module also proceeds as follows:
When the weights of this present node more than or equal to these maximum weights
Figure FDA0000146630200000024
And this present node is positioned at N TDuring layer, finish search.
10. single tree search List Sphere Decoder device as claimed in claim 8 is characterized in that, described the second search module also proceeds as follows:
When the weights of this present node less than these maximum weights
Figure FDA0000146630200000025
And when present node is positioned at the 1st layer, upgrade described maximum weights
Figure FDA0000146630200000026
And the path at present node place is replaced
Figure FDA0000146630200000027
The path, place forms new K paths.
11. single tree search List Sphere Decoder device as claimed in claim 10 is characterized in that the maximum weights after the renewal
Figure FDA0000146630200000028
Value for weights maximum in the new K paths.
12. single tree search List Sphere Decoder device as claimed in claim 10 is characterized in that, described the second search module also proceeds as follows:
After search finishes, be not updated if exist
Figure FDA0000146630200000029
Then use greater than maximum weights
Figure FDA00001466302000000210
In a right value update of minimum.
13. single tree search List Sphere Decoder device as claimed in claim 8 is characterized in that described K value is chosen according to the input simulation result.
14. such as the described single tree search List Sphere Decoder device of in the claim 8 to 13 each, it is characterized in that, also comprise correcting module, after finishing in order to search, be not updated if exist Then use greater than maximum weights In a right value update of minimum.
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