CN102594467B - Receiver detection method for wireless multiple input multiple output system - Google Patents

Abstract

The invention provides a receiver detection method of a wireless multiple-input multiple-output system, comprising the following steps: performing QR decomposition on a channel matrix H to obtain a Q matrix and an upper triangular matrix R; converting the conjugate transposition of the Q matrix to the received signal vector Multiply y to get the equalized signal of the received signal Establish the lookup table LUT of node expansion sequence, set the radius of spherical decoding, and the available storage space is M; calculate the search center, obtain the subscript of the lookup table according to the search center and modulation mode, and obtain the initially selected node; move from the stack Remove the selected node, and expand sibling nodes and child nodes according to the selected node; judge whether the selected node is a leaf node, and maintain the soft value table; select the node selected for the next iteration; calculate the LLR value according to the soft value table. The invention effectively reduces the complexity of the receiver under the premise of ensuring the system performance.

Description

The receiver detection method of wireless multiple-input-multiple-output systems

Technical field

What the present invention relates to is a kind of method of wireless communication technology field, relates to particularly a kind of receiver detection method of wireless multiple-input-multiple-output systems.

Background technology

Traditional multiple-input and multiple-output (Multiple Input Multiple Output, MIMO) technology, it is the multi-antenna structure by utilizing base station and user side, realize diversity and space division multiplexing, thereby increase a kind of technology of throughput of system, because become 3GPP-LTE, the study hotspot of IEEE 802.16e WIMAX.In recent years, along with the development of turbo decoding and LDPC decoding technique, the combination of the decoding of MIMO receiver and channel decoding technology can significantly reduce the error rate of system.Maximum Likelihood Detection (ML) is optimum receiver detection algorithm, but the complexity that ML detects increases along with number of transmit antennas and order of modulation are exponential type.The linearity test algorithm of suboptimum, as ZF (ZF) and least mean-square error (MMSE) criterion, although complexity is low, all can not reaches and receive full marks intensity, and performance is far below ML detection algorithm.Soft output method for detecting spherical decode in multi-input multi-output system can significantly reduce the complexity of system.

Through existing literature search is found, Studer.C etc. are at " IEEE Transaction on Information Theory " (U.S. electric and Electronic Engineering Association's information theory periodical, October the 56th in 2010 the 4827th to the 4842nd page of volume) on, deliver " Soft-Input Soft-Output Single Tree-Search Sphere Decoding " (" soft input globular decoding of soft output of single tree search "), this article has proposed, in many antennas multi-input multi-output system, in search procedure, dynamically update soft value and ML value of sentencing firmly of each bit, the document has proved that STS Sphere Decoding Algorithm performance is optimum, and it is once accessed to ensure that each node only needs, but still complexity is higher for this soft output globular decoding, be difficult for realizing.Find through retrieval again, at " Signal Processing ", (signal is processed magazine to Markus M etc., October the 90th in 2010 the 2863rd page to the 2876th page of volume) on, deliver " Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems " (" application of list globular decoding in MIMO-OFDM system "), this article calculates soft value by the list globular decoding of K-Best globular decoding or Dijkstra (Di Jiesitela) Sphere Decoding Algorithm, but list globular decoding can not ensure in list ML firmly the bit polarity of the value of sentencing necessarily have contrary, cause the error of calculation of LLR value, affect systematic function.

Summary of the invention

The object of the invention is to overcome the above-mentioned deficiency of prior art, a kind of soft output Sphere Decoding Algorithm of low complex degree is provided, reduce the complexity of receiver.The present invention is generalized to the soft output globular decoding of single tree search in conjunction with Dijkstra Sphere Decoding Algorithm, by structure look-up table LUT (Look Up Table), improve the node expanded search algorithm of Dijkstra globular decoding, obtain rapidly the ML value of sentencing and LLR value firmly, reduce search volume, reduced Receiver Complexity, and performance detects with ML consistent.The present invention has in accurate ML performance and the low feature of complexity, and is suitable for the feature of various multi-input multi-output systems.

According to an aspect of the present invention, the receiver detection method of described wireless multiple-input-multiple-output systems comprises the following steps:

Step 1: channel matrix H is carried out to QR decomposition, obtain Q matrix and upper triangular matrix R; The conjugate transpose of Q matrix and received signal vector y are multiplied each other, obtain receiving the equalizing signal of signal

Step 2: the look-up table LUT (Look Up Table) that sets up node extended order;

Step 3: calculate search center, obtain the subscript of look-up table according to search center and modulation system, initially chosen node, be pressed in stack;

Step 4: remove from stack and choose node, and according to the expansion brotgher of node of choosing node, upgrade radius, judge whether the radius of present node is less than current search radius, be to cut down present node and all branches thereof, otherwise the brotgher of node is pressed in stack;

Step 5: judge and choose whether node is leaf node, if leaf node, carry out the maintenance of soft value table, otherwise one deck expanded search downwards, calculate the subscript that search center and modulation system obtain look-up table, obtain child node, judge whether the radius of present node is less than current search radius, be to cut down present node and all branches thereof, otherwise child node is pressed in stack;

Step 6: whether disconnected current stack is empty, empty if, arrives step 7; Otherwise according in stack memory space and the node chosen of Weight selected next iteration, return to step 5;

Step 7: carry out LLR value according to soft value table and calculate.

Preferably, in described step 2, look-up table establishment step is:

Hypothesis tree searches i layer, part vector

known, definition c _iit is the search center of i layer

$c_i=\frac{1}{r_{\mathrm{ii}}}({\stackrel{\·}{y}}_i-\underset{n=i+1}{\overset{N_R}{\mathrm{\Σ}}}{r}_{\mathrm{in}}{s}_n)$ Formula one

Described formula one is rewritten as:

$d_i=d_{i+1}+{\mathrm{<figure-callout\; id="2"\; label="r\; ii"\; filenames="HDA0000137383210000011.png"\; state="\{\{state\}\}">r</figure-callout>}}_{\mathrm{ii}}^{}{|c_i-s_i|}^2$ Formula two

First node accesses range search center c _inearest constellation point s _i, then according to c _iorder from the near to the remote carries out sorted search access; Determine when the modulation system of mimo system, modulation symbol collection is definite, thereby can be according to c _iarrange the extended order of constellation point in belonging positions region, sets up look-up table LUT; Wherein, the region of each constellation point is divided into 4, from the near to the remote the access order of constellation point is arranged according to the distance in space, the look-up table size that 4-QAM needs is 16, the look-up table size that 16-QAM needs is that the look-up table size that 64,64-QAM needs is 256.

Preferably, in described step 3 and step 5, search center computational process is:

Hypothesis tree searches i layer, part vector

known, definition c _iit is the search center of i layer

$c_i=\frac{1}{r_{\mathrm{ii}}}({\stackrel{\&CenterDot;}{y}}_i-\underset{n=i+1}{\overset{N_R}{\mathrm{\&Sigma;}}}{r}_{\mathrm{in}}{s}_n).$

Preferably, in described step 3 and step 4, node expansion process is:

Suppose that it is N that current stack is wanted the node of downward one deck expansion _c=(s=s ⁽ⁱ⁾d (s), p, q, i), in stack, remaining space is M, wherein s is current solution vector, and i is the number of plies that present node is positioned at, and p and q are respectively the subscript in the constellation point of i layer at the subscript of current selected look-up table and present node, wherein, node extended method comprises following sub-step:

1) from stack, remove node N _c=(s=s ⁽ⁱ⁾, d (s), p, q, level=i), M=M+1, the node that expansion level=i layer and node N are nearest, even s _f=(LUT (p, q+1), s ⁽ⁱ⁺¹⁾), calculate d (s _f), if d is (s _f) < R ₀by node N _f=(s=s _f, d (s _f), p, q=q+1, level=i) deposit in stack M=M-1 in;

2) if node N _cbe leaf node while being i=1, carry out that LLR value is upgraded and ML separates and upgrades, otherwise downward one deck is expanded, pass through following formula

$d_i=d_{i+1}+{\mathrm{<figure-callout\; id="2"\; label="r\; ii"\; filenames="HDA0000137383210000011.png"\; state="\{\{state\}\}">r</figure-callout>}}_{\mathrm{ii}}^{}{|c_i-s_i|}^2$

Calculate search center c _i-1, obtain look-up table subscript p, make s _exp=(LUT (p, 0), s ⁽ⁱ⁾), calculate d (s _exp), if d is (s _exp) < R ₀by node N _exp=(s=s _exp, d (s _exp), p, q=0, i-1) deposit in stack M=M-1 in.

Preferably, in described step 3 and step 4, radius renewal process is:

Radius upgrades and is divided into two parts:

1) for known portions vector, i.e. k>=i, m=1,2 ..., Q, radius R ₀with

's

relevant,

${\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="HDA0000137383210000011.png,HDA0000137383210000021.png"\; state="\{\{state\}\}">R</figure-callout>}}_0=\max \left\{d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)\right|\&ForAll;k\&GreaterEqual;i,m=\mathrm{1,2}...,Q,s_{k,m}=s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\};$

2) for the part vector of also not searching for, i.e. k < i, m=1,2 ..., Q, radius R ₀with all relevant,

${\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="HDA0000137383210000011.png,HDA0000137383210000021.png"\; state="\{\{state\}\}">R</figure-callout>}}_0=\max \left\{d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)\right|\&ForAll;k<i,m=\mathrm{1,2},...,Q\}.$

Preferably, in described step 4, soft value table renewal process is:

Be initialized as $d\left(s^{\mathrm{ML}}\right)=d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)=\&infin;(\&ForAll;k,m),$ In two kinds of situation:

1) in the time obtaining a new ML and separate, i.e. d (s) < d (s ^mL), meet all

bit

be updated to

and upgrade s ^mL=s and d (s ^mL)=d (s);

2) if d (s) > d is (s ^mL), now only need to upgrade

value, when

and

time, upgrade

$d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)=d\left(s\right).$

Preferably, in described step 6, node selecting method is:

Make n _max=min (M+2, N _t), the node in stack is sorted from small to large according to d (s), select to there is minimum d (s) and meet i < n _maxnode, establishing the node of choosing is N _c.

Preferably, in described step 7, LLR value calculating method is:

For $\&ForAll;k\&Element;\{1,...,N_T\},$ $\&ForAll;m\&Element;\{1,...,Q\},$ Utilize following formula to calculate LLR value

$L\left(b_{k,m}\right)=\left\{\begin{array}{c}d\left(s^{\mathrm{ML}}\right)-d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right),s_{k,m}^{\mathrm{ML}}=-1\\ d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)-d\left(s^{\mathrm{ML}}\right),s_{k,m}^{\mathrm{ML}}=+1\end{array}\right..$

Brief description of the drawings

Figure 1 shows that the schematic diagram that the inventive method look-up table is set up;

Figure 2 shows that under 4 × 4MIMO system, under QPSK modulation, the error rate contrast schematic diagram of different receivers detection method;

Figure 3 shows that under 4 × 4MIMO system, under 16QAM modulation, the error rate contrast schematic diagram of different receivers detection method;

Figure 4 shows that under 4 × 4MIMO system, under QPSK modulation, the search volume of different receivers detection method is to when complexity contrast schematic diagram;

Figure 5 shows that under 4 × 4MIMO system, under 16QAM modulation, the search volume of different receivers detection method is to when complexity contrast schematic diagram;

Figure 6 shows that wireless multiple-input-multiple-output systems block diagram;

Figure 7 shows that performing step block diagram of the present invention.

Embodiment

With reference to the accompanying drawings embodiments of the invention are elaborated, in description process, having omitted is unnecessary details and function for the present invention, obscures to prevent that the understanding of the present invention from causing.Provide specific embodiments of the invention below, be applicable to long evolving system and advanced long evolving system.It should be noted that, the invention is not restricted to the application described in embodiment, the wireless communication system that also uses MIMO technique and receive detection technique applicable to other.

Embodiment

For making object of the present invention, technical scheme and advantage are clearer, describe the present invention below in conjunction with accompanying drawing and concrete embodiment.

The system model that the present embodiment adopts is for having N _tindividual transmitting antenna N _rthe mimo system of individual reception antenna, without loss of generality, makes N _r=N _t, transmitting information bit is by channel coding module, interleaver module and string and change modulation module after obtain N _t× 1 emission signal vector

wherein Ω is modulation symbol collection, | Ω |=2 ^q(Q is order of modulation).Corresponding N _r× 1 received signal vector

so the model of mimo system is:

y＝Hs+n (1)

H in formula (1) is channel matrix, and n represents that average is 0, and variance is σ ²gauss's additive white noise.

Channel matrix H is carried out to QR decomposition and obtain H=QR, wherein R is upper triangular matrix, and Q is orthogonal matrix, and formula (1) formula can be write as

$\stackrel{\&CenterDot;}{y}=\mathrm{Rs}+\stackrel{\&CenterDot;}{n}---\left(2\right)$

Wherein

definition

for vector

i element, r _ijfor (i, j) individual element of upper triangular matrix R.Definitional part signal vector

vector s ⁽ⁱ⁾regard the node of one tree as, establish i=N _t+ 1 layer is root node, and i=1 layer is leaf node, and each node has 2 ^qindividual child node, each leaf node s ⁽¹⁾it is all a solution vector.Euclidean distance

can obtain by part of Euclidean distance (PED) iterative computation:

d _i＝d _i+1+|e _i| ²，i＝N _T，N _T-1，…，1 (3)

${\left|e_i\right|}^2={|{\stackrel{\&CenterDot;}{y}}_n-\underset{n=i+1}{\overset{N_R}{\mathrm{\&Sigma;}}}{r}_{\mathrm{in}}{s}_n-r_{\mathrm{ii}}{s}_i|}^2---\left(4\right)$

Wherein d (s)=d ₁,

be initialized as 0.

MIMO receiver is exported the Bit data b that makes a start _{k, m}, k ∈ 1 ..., N _t, m ∈ 1 ..., the soft value estimation of Q}, the definition of the soft value LLR (Log Likelihood Ratio) of max-log is:

$L\left(b_{k,m}\right)\&ap;\frac{1}{{\mathrm{\&sigma;}}_n^2}[\underset{s\&Element;S_{k,m}^{-1}}{\min }{\left|\right|y-\mathrm{Hs}\left|\right|}^2-\underset{s\&Element;S_{k,m}^{+1}}{\min }{\left|\right|y-\mathrm{Hs}\left|\right|}^2]---\left(5\right)$

$\&ap;\frac{1}{{\mathrm{\&sigma;}}_n^2}[\underset{s\&Element;S_{k,m}^{-1}}{\min }{\left|\right|\stackrel{\&CenterDot;}{y}-\mathrm{Rs}\left|\right|}^2-\underset{s\&Element;S_{k,m}^{+1}}{\min }{\left|\right|\stackrel{\&CenterDot;}{y}-\mathrm{Rs}\left|\right|}^2]$

In formula (5), L (b _{k, m}) represent that transmitting is to quantity symbol s _kthe LLR value of m bit,

represent the symbol s in vectorial s _kthe value of m bit is ± 1 set.Observed and can be obtained by formula (5), one of them minimum value is that ML separates s ^mLeuclidean distance $d\left(s^{\mathrm{ML}}\right)={\left|\right|\stackrel{\&CenterDot;}{y}-{\mathrm{Rs}}^{\mathrm{ML}}\left|\right|}^2,$ Wherein

$s^{\mathrm{ML}}=\underset{s\&Element;{\mathrm{\&Omega;}}^{N_T}}{\arg \min }{\left|\right|\stackrel{\&CenterDot;}{y}-\mathrm{Rs}\left|\right|}^2---\left(6\right)$

Another corresponding minimum value is:

$\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)=\underset{s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}}{\min }{\left|\right|\stackrel{\&CenterDot;}{y}-\mathrm{Rs}\left|\right|}^2---\left(7\right)$

for transmitting is to quantity symbol s _kthe polarity of m bit is s ^mLcontrary.So formula (2) can be rewritten as:

$L\left(b_{k,m}\right)=\left\{\begin{array}{c}d\left(s^{\mathrm{ML}}\right)-d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right),s_{k,m}^{\mathrm{ML}}=-1\\ d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)-d\left(s^{\mathrm{ML}}\right),s_{k,m}^{\mathrm{ML}}=+1\end{array}\right.---\left(8\right)$

If set

for meeting transmitting to quantity symbol s _kthe polarity of m bit is s ^mLthe set of contrary condition, the search procedure that MIMO Maximum Likelihood Detection and max-log LLR value computational process can cost trees of equivalent place, is gathering

and set

middle search has the leaf node of minimum Eustachian distance, obtains d (s ^mL) and N _t× Q

value.

The embodiment of the present invention has adopted the Dijkstra soft output method for detecting spherical decode preferential based on tolerance, as shown in Figure 7, comprises the steps:

Step 201: channel matrix H is carried out to QR decomposition, obtain Q matrix and upper triangular matrix R.

Step 202: the conjugate transpose of Q matrix and received signal vector y are multiplied each other, obtain receiving the equalizing signal of signal

by to equivalent matrix R and equalizing signal

the search tree building carries out soft output globular decoding.

Step 203: set up look-up table LUT, the radius R of globular decoding is set ₀=∞, available storage size is M.

Concrete look-up table LUT process of establishing is that hypothesis tree searches i layer, part vector

known, definition c _iit is the search center of i layer

$c_i=\frac{1}{r_{\mathrm{ii}}}({\stackrel{\&CenterDot;}{y}}_i-\underset{n=i+1}{\overset{N_R}{\mathrm{\&Sigma;}}}{r}_{\mathrm{in}}{s}_n)---\left(9\right)$

Formula (9) is rewritten as:

$d_i=d_{i+1}+{\mathrm{<figure-callout\; id="2"\; label="r\; ii"\; filenames="HDA0000137383210000011.png"\; state="\{\{state\}\}">r</figure-callout>}}_{\mathrm{ii}}^{}{|c_i-s_i|}^2---\left(10\right)$

First node accesses range search center c _inearest constellation point s _i, then according to c _iorder from the near to the remote carries out sorted search access.Determine when the modulation system of mimo system, modulation symbol collection is definite, thereby can be according to c _iarrange the extended order of constellation point in belonging positions region, sets up look-up table LUT.For reducing the memory space of look-up table, the region of each constellation point is divided into 4 by the present invention, from the near to the remote the access order of constellation point arranged according to the distance in space, as shown in Figure 1, is modulated to example with 16QAM, as search center c _iwhile falling into diagonal line hatches region, 16 constellation point are according to the distance-taxis of sequencing and shadow region, wherein, this order can change according to actual needs, this does not affect flesh and blood of the present invention, and for example, in Fig. 1 (a), the order of constellation point " 9 " and " 10 " is just likely exchanged, then simulation result shows, this approximate processing does not almost affect performance.In order to cover all possible constellation point in QAM modulation, the look-up table size that 4-QAM needs is that the look-up table size that 16,16-QAM needs is that the look-up table size that 64,64-QAM needs is 256.

Step 204: calculate

according to

and modulation system obtains the subscript p of look-up table, obtain initial point

if memory node will $N_c=(s=s^{\left(N_T\right)},d\left(s\right),p,q=0,i=N_R),$ M＝M-1。

Step 205: remove node N from stack _c=(s=s ⁽ⁱ⁾, d (s), p, q, i), M=M+1, expansion i layer and node N _cnearest node, makes s _f=(LUT (p, q+1), s ⁽ⁱ⁺¹⁾), calculate d (s _f) according to current vectorial s _fupgrade the radius R of globular decoding ₀.If d is (s _f) < R ₀, by N _f=(s=s _f, d (s _f), p, q=q+1, i) deposit in stack M=M-1 in; Otherwise forward step 206 to.

The radius update method of the globular decoding of this step is:

When search tree obtains partial solution vector

time, so upgrading, the radius of globular decoding is divided into two parts

(1) for known portions vector, i.e. k>=i, m=1,2 ..., Q, radius R ₀with

's

relevant,

${\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="HDA0000137383210000011.png,HDA0000137383210000021.png"\; state="\{\{state\}\}">R</figure-callout>}}_0=\max \left\{d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)\right|\&ForAll;k\&GreaterEqual;i,m=\mathrm{1,2},...,Q,s_{k,m}=s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\}$

(2) for the part vector of also not searching for, i.e. k < i, m=1,2 ..., Q, radius R ₀with all

relevant,

${\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="HDA0000137383210000011.png,HDA0000137383210000021.png"\; state="\{\{state\}\}">R</figure-callout>}}_0=\max \left\{d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)\right|\&ForAll;k<i,m=\mathrm{1,2},...,Q\}$

Step 206: if node N _cfor leaf node (i=1), upgrade soft value table

and s ^mL; Otherwise one deck expansion downwards, through type (10) calculates search center c _i-1, obtain look-up table subscript p, make s _exp=(LUT (p, 0), s ⁽ⁱ⁾), calculate d (s _exp), according to s _expupgrade radius R ₀(radius update method is consistent with step 205).If d is (s _exp) < R ₀, by N _exp=(s=s _exp, d (s _exp), p, q=0, i-1) deposit in stack M=M-1 in; Otherwise forward step 207 to.

The process of the soft value table of renewal in this step is:

Initialization

in the time that arriving leaf node, tree search obtains new solution vector s ⁽¹⁾just carry out soft value table and upgrade, in two kinds of situation:

(1) in the time obtaining a new ML and separate, i.e. d (s) < d (s ^mL), meet all

bit

be updated to

and upgrade s ^mL=s and d (s ^mL)=d (s).Ensure like this current at all and s ^mLcontrary bit

it is all current minimum value.

(2) if d (s) > d is (s ^mL), now only need to upgrade

value, when

and

time, with new $d\left(s_{k,m}^{\stackrel{\&OverBar;}{\mathrm{ML}}}\right)=d\left(s\right).$

Step 207: make n _max=min (M+2, N _t), the node in stack is sorted from small to large according to d (s), select to there is the node of minimum d (s) and meet number of plies i < n _max, establishing the node of choosing is N _c.If current stack is not M ≠ 0 for sky,, forward step 3 to; Otherwise forward step 205 to.

Step 208: the LLR value L (b that calculates each bit according to formula (5) _{k, m}), Output rusults.

The Sphere Decoding Algorithm that Fig. 2, Fig. 3, Fig. 4, Fig. 5 are the embodiment of the present invention and traditional several Sphere Decoding Algorithm performances and the comparison of complexity.

Simulation parameter is as follows: channel adopts suburb macrocell SCM channel, and carrier frequency is 2GHz, and bandwidth is 3MHz, and chnnel coding adopts turbo coding, and modulation system is QPSK and 16QAM, and code check is 378/1024, number of transmit antennas: 4, and reception antenna number: 4.

Fig. 2, Fig. 3 have provided the ber curve comparison of soft-decision under distinct methods, the interpretation method that the present invention proposes is almost consistent with the performance of STS spherical decoding method method, than K-BEST list spherical decoding method, performance has larger lifting, and under high order modulation 16QAM, performance boost is more obvious.

Fig. 4, Fig. 5 have provided the comparison of the complexity of different receivers detection method, complexity be relatively the number comparison by search node in search procedure, from Fig. 4, Fig. 5, the interstitial content of interpretation method search and the downward trend of STS-SD interpretation method that the present invention proposes are similar, but compared with STS-SD interpretation method, the nodes that search volume was accessed reduces, and the amplitude of variation of the nodes of accessing is compared little with STS-SD interpretation method, the hardware that is beneficial to estimating system is realized expense, decoding delay and throughput.Compared with being beneficial to hard-wired K-BSET list globular decoding, the spherical decoding method that the present invention proposes is under low-order-modulated (QPSK), and performance is better compared with K-BSET (64) spherical decoding method, and search node number still less.Under high order modulation (16QAM), performance is much better than K-BSET (16) spherical decoding method, and search node to count increasing degree very little, hardware realize and performance on have good compromise.

From Fig. 2, Fig. 3, Fig. 4, Fig. 5 comparative illustration, example of the present invention is keeping, under high performance prerequisite, effectively reducing the complexity of system.

Claims (4)

1. A receiver detection method for a wireless MIMO system, characterized in that it comprises: Step 1: Perform QR decomposition on the channel matrix H to obtain the Q matrix and upper triangular matrix R, and multiply the conjugate transpose of the Q matrix with the received signal vector y to obtain the balanced signal of the received signal Step 2: Establish a lookup table LUT for the node expansion sequence, set the radius of the spherical decoding, and the available storage space is M; Step 3: Calculate the search center, get the subscript of the lookup table LUT according to the search center and modulation method, get the initial selected node, and push it into the stack; Step 4: Remove the selected node from the stack, and update the radius of the spherical decoding according to the extended sibling nodes of the selected node, and judge whether the radius of the current node is smaller than the current search radius, if so, cut off the current node and all its branches, otherwise Push sibling nodes into the stack; Step 5: Determine whether the selected node is a leaf node, if it is a leaf node, maintain the soft value table, otherwise expand the search to the next layer, calculate the search center and modulation method to obtain the lookup table LUT subscript to get the child node, judge whether the radius of the current node is smaller than the current search radius, if yes, cut off the current node and all its branches, otherwise push the child node into the stack;  Step 6: Determine whether the current stack is empty, if it is empty, go to step 7; otherwise, select the node selected for the next iteration according to the storage space and weight in the stack, and return to step 5; Step 7: Calculate the logarithmic likelihood ratio LLR value according to the soft value table. the 2. the receiver detection method of wireless MIMO system according to claim 1, is characterized in that, In said step 2, the look-up table establishment step is: Assuming that the tree searches to the i-th level, the partial vector

Known, define c _i as the search center of the i-th layer

formula one Then the formula one is rewritten as:

formula two The node first visits the constellation point _si closest to the search center _ci , and then searches and visits according to the order from closest to far from _ci ; when the modulation mode of the MIMO system is determined, the modulation symbol set is determined, so it can be determined according to _ci Arrange the expansion order of the constellation points in the location area to which they belong, and establish a lookup table LUT; among them, the area of each constellation point is divided into 4 blocks, and the access order of the constellation points is arranged according to the spatial distance from near to far. 4-QAM The required lookup table size is 16, the required lookup table size for 16-QAM is 64, and the required lookup table size for 64-QAM is 256; in: _NR is the number of receiving antennas; r _ii is the (i, i)th element of the upper triangular matrix R;

as a vector

The i-th element of ; r _in is the (i, n)th element of the upper triangular matrix R; S _n is the nth element of the partial vector s ⁽ⁱ⁺¹⁾ ; d _i , d _i+1 are the partial Euclidean distances before and after iteration respectively. 3. the receiver detection method of wireless MIMO system according to claim 1, is characterized in that, In the above step 3 and step 5, the calculation process of the search center is: Assuming that the tree searches to the i-th level, the partial vector

Known, define c _i as the search center of the i-th layer in: _NR is the number of receiving antennas; r _ii is the (i, i)th element of the upper triangular matrix R; as a vector

The i-th element of ; r _in is the (i, n)th element of the upper triangular matrix R; S _n is the nth element of the partial vector s ⁽ⁱ⁺¹⁾ . 4. the receiver detection method of wireless MIMO system according to claim 1, is characterized in that, In the above step 3 and step 4, the node expansion process is: Assume that the node to be expanded to the next layer in the current stack is N _c = (s=s ⁽ⁱ⁾ , d(s), p, q, i), and the remaining space in the stack is M, where s is the current solution vector , i is the number of layers where the current node is located, p and q are respectively the subscript of the currently selected lookup table and the subscript of the constellation point of the current node at layer i, wherein the node expansion method includes the following substeps: 1) Remove node N from the stack _c = (s=s ⁽ⁱ⁾ , d(s), p, q, level=i), M=M+1, expand level=i layer closest to node N node, that is, let s _f =(LUT(p,q+1),s ⁽ⁱ⁺¹⁾ ), calculate d(s _f ), if d(s _f )<R ₀ , the node N _f =(s=s _f ,d(s _f ),p,q=q+1,level=i) are stored in the stack, M=M-1; 2) If the node N _c is a leaf node i=1, update the LLR value and update the ML solution, otherwise it will expand to the next layer, through the following formula Calculate the search center c _i-1 to obtain the subscript p of the lookup table, let s _exp =(LUT(p,0),s ⁽ⁱ⁾ ), calculate d(s _exp ), if d(s _exp )＜R ₀ will Node N _exp = (s = s _exp , d (s _exp ), p, q = 0, i-1) is stored in the stack, M = M-1; in: s ⁽ⁱ⁾ is a partial signal vector; s _f is the current vector; LUT(p,q) is the node whose subscript is q in the constellation point subscript of the i-th layer in the currently selected lookup table with subscript p; R ₀ is the radius of spherical decoding; ML stands for maximum likelihood estimation. the

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