Source: http://www.google.com/patents/US7978759?dq=%235,519,867
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Patent US7978759 - Scalable equalizer for multiple-in-multiple-out (MIMO) wireless transmission - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsSystems and techniques relating to processing information received from a spatially diverse transmission. In some implementations, a method comprises: obtaining a received signal that was transmitted over a wireless channel using spatially diverse transmission, the received signal comprising multiple...http://www.google.com/patents/US7978759?utm_source=gb-gplus-sharePatent US7978759 - Scalable equalizer for multiple-in-multiple-out (MIMO) wireless transmissionAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS7978759 B1Publication typeGrantApplication numberUS 11/222,490Publication dateJul 12, 2011Filing dateSep 7, 2005Priority dateMar 24, 2005Fee statusPaidAlso published asUS8204103Publication number11222490, 222490, US 7978759 B1, US 7978759B1, US-B1-7978759, US7978759 B1, US7978759B1InventorsKonstantinos SarrigeorgidisOriginal AssigneeMarvell International Ltd.Export CitationBiBTeX, EndNote, RefManPatent Citations (30), Non-Patent Citations (20), Referenced by (6), Classifications (22), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetScalable equalizer for multiple-in-multiple-out (MIMO) wireless transmission
SNR k = E s n t N o ·   Q k h k   2 . The array gain is given by E[PRX/PTX]=nr−nt+1.
x ^ i = 1 ( H * · H ) ii - 1 ( H * · H ) - 1 · H * · y = W · y , 1 ≤ i ≤ n t ( 4 ) W ll , i = 1 ( H * · H ) ii - 1 1 ≤ i ≤ n t ( 5 ) The channel matrix H can be estimated column by column by processing the long training fields (LTFs). In this case, the complete channel matrix H is not known until the last LTF has been processed, but the equalizer 340 would traditionally need the complete channel matrix H to compute the quantities for equalization. Waiting until the last LTF is processed is generally undesirable, as this can result in strict hardware and timing requirements.
W = 1 diag ( H * · H ) - 1 ( H * · H ) - 1 · H * . The equalizer coefficients using a direct method can then be computed as follows:
W = ( h 11 h 22 - h 12 h 21 ) * [ 1 ( h 22 2 + h 12 2 ) 0 0 1 ( h 11 2 + h 21 2 ) ] [ h 22 - h 12 - h 21 h 11 ] . ( 6 ) This is a likely implementation choice if only a 2×2 system is of interest.
H = [ h 11 h 12 h 21 h 22 h 31 h 32 ] = [ h 1 h 2 ] , then the equalizer matrix W is given by matrix 400 shown in FIG. 4. As can be seen, the structure of this equalizer matrix is different from the structure of the 2×2 equalizer matrix), and thus configuring the architecture to support both a 2×2 and 2×3 mode may be problematic. This direct approach essentially breaks down for 3×3 MIMO and 4×4 MIMO systems, where the equalizer computation has O(n3) complexity. For example, an analytical expression of the ZF equalizer coefficients for a 4×4 MIMO system, found using the MATHEMATICA® software package, was 10 pages in length. Moreover, the boxed elements 410 in FIG. 4 are estimated during the first LTF. As can be seen in this example, the equalizer matrix computations cannot be started unless the second LTF is received and processed. Thus, the computation cannot be readily distributed in time.
x ^ = G · Q 2 × 2 * y = [ r 11 r 22 2 c - r 12 r 11 r 22 c 0 r 22 ] Q 2 × 2 * y ( 7 ) For the 2×3 case, the equalized vector is given by:
x ^ = G · Q 2 × 3 * y = [ r 11 r 22 2 c - r 12 r 11 r 22 c 0 r 22 ] Q 2 × 3 * y ( 8 ) where the rij elements result from the QR decomposition of the channel matrix. As can be seen, the G matrices are the same for 2×2 and 2×3, and the CORDIC based Givens rotations captured in the Q matrices can be configured to support the 2×2 and 2×3 modes. Therefore, a QR based equalization approach can be a more promising solution when scalability and configurability is a design issue.
{ ∑ i = 0 nt - 1 ( 2 n r - ( 2 i + 1 ) ) } Q = 2 n r n t - n t 2 . That is, the CORDIC based QR decomposition minimizes the amount of free variables needed to construct the matrix Q. Yet, the number of computations used in the equalization step Q*y is significantly reduced by using CORDIC Givens rotations instead of Gram-Schmidt. The number of real computations for Q matrix equalization can be reduced from 4ntnr (GS based) to 3nrnt−nt(3nt+1)/2 (CORDIC based). For a 4×4 system, there can be up to a 65% reduction in computations by employing CORDIC QR.
MIMO MODE 2 × 2 2 × 3 3 × 3 3 × 4 4 × 4 Q GS DOF : 2ntnr 8 12 18 24 32 Q CORDIC DOF : 2nrnt − nt 2 4 8 9 15 16 Memory Savings with QR CORDIC 50% 33% 50% 37% 50% Thus, memory requirements for Q storage can be reduced across different spatial diversity MIMO configurations by employing a QR CORDIC implementation.
Increase from 2 × 2 mode
G 2 × 2 = W ll · R 2 × 2 - 1 = [ r 11 r 22 2 c - r 12 r 11 r 22 c 0 r 22 ] , x ^ = G · z ( 10 ) The substream SNRs can be written as: wl1=r11 2r22 2/c, wl2=r22 2.
P = [ r 11 0 0 1 ] , R ^ = [ r 22 - r 12 0 1 ] . P is a diagonal matrix, and {circumflex over (R)} is an upper triangular matrix. Letting c=r22 2+|r12|2, an order recursive computational framework can be developed that calculates the coefficients of any equalizer matrix G up to a predefined limit (e.g., up to 4×4), by recursively updating the two kernel matrices while minimizing the amount of extra computations that involve elements of the upper triangular matrix R.
G 2 × 2 = r 22 [ r 11 0 0 1 ] [ 1 c 0 0 1 ] [ r 22 - r 12 0 1 ] = [ P D ^ ] R ^ , ( 11 ) where P→r22P and {circumflex over (R)}→{circumflex over (R)}. From this, it can be seen that
G 3 × 3 = W ll · R 3 × 3 - 1 = [ r 11 r 22 2 r 33 2 c 1 - r 11 r 12 r 22 r 23 2 c 1 r 11 r 22 r 33 ( r 12 r 23 - r 13 r 22 ) c 1 0 r 22 r 33 2 c 2 - r 22 r 23 r 33 c 2 0 0 r 33 ] , ( 12 ) letting u=[−r13 r23]T, v=[r22 r12]T, and α1=vTu.
D ^ = [ 1 c 1 0 0 0 1 c 2 0 0 0 1 ] , r 33 2 c +  α 1  2 -> c 1 , c 2 = r 33 2 · 1 +  r 23  2 ( 13 ) The {circumflex over (R)} matrix can be order updated using
[ α 1 r 33 R ^ 0 - r 23 1 ] -> R ^ , ( 14 ) the equalizer matrix can be computed as G3×3=D{circumflex over (R)}, and the substream SNRs can be given by
[ α 1 r 44 R ^ α 2 0 - r 34 1 ] -> R ^ , ( 16 ) the equalizer matrix can be computed as G4×4=D{circumflex over (R)}, and the substream SNRs can be given by Wll=P[P{circumflex over (D)}].
R ^ 2 × 2 = [ r 22 - r 12 0 1 ] . ( 20 ) It can be seen that the degrees of freedom (DOF) of the matrices {circumflex over (R)}n t ×n t are nt 2−1. Thus, for 2×2 and 2×3 MIMO, there are 3 DOF: three real multiplies to calculate the G matrix.
R ^ 3 × 3 = [ α 1 r 33 R ^ 2 × 2 0 - r 23 1 ] ( 21 ) {circumflex over (R)}3×3 has DOF 8, so a second processing unit, G-PE2, can be used in combination with G-PE1 to compute G3×3=D3×3{circumflex over (R)}3×3. This G-matrix processing can be performed during the third preamble processing.
R ^ 4 × 4 = [ α 1 r 44 R ^ 3 × 3 α 2 0 - r 34 1 ] ( 22 ) {circumflex over (R)}4×4 has DOF 15, so a third processing unit, G-PE3, can be used in combination with G-PE1 and G-PE3 to compute G4×4=D4×4{circumflex over (R)}4×4. This G-matrix processing can be performed during the fourth preamble processing.
MIMO MODE 2 × 2 2 × 3 3 × 3 4 × 4 CORDIC processors 3 3 3 6 DIV units 1 1 2 3 D-Matrix (last D-PE1 D-PE1 D-PE1 D-PE1 preamble 5 MUL 5 MUL D-PE2 D-PE2 processing) 16 MUL D-PE3 33 MUL G-Matrix G-PE1 G-PE1 G-PE1 G-PE1 3 MUL 3 MUL G-PE2 G-PE2 11 MUL G-PE3 23 MUL Total MUL units 8 8 27 56 Although separate processing units (D-PE1, D-PE2, D-PE3, G-PE1, G-PE2, and G-PE3) are shown and described in connection with the calculations of D and G matrices, it will be appreciated that these processing units need not be discrete components and may be integrated into one or more units.
MIMO MODE 2 × 2 2 × 3 3 × 3 3 × 4 4 × 4 Last Preamble CORDIC ops 5 11 12 20 22 Last Preamble MUL ops 8 8 27 56 56 DATA field CORDIC ops 5 11 12 20 22 DATA field MUL ops 8 8 18 32 32 Moreover, the G-PE processing units can be reused for the G matrix equalization, {circumflex over (x)}=G·z, during DATA processing.
Area 2 × 3 Area 3 × 3 Area 4 × 4 Module (Lambda {circumflex over ( )}2) (Lambda {circumflex over ( )}2) (Lambda {circumflex over ( )}2) CORDIC proc 111000000 111000000 2 × 111000000 MUL Group 70000000 3 × 70000000 7 × 70000000 Q Memory 48000000 1.25 × 48000000 2 × 48000000 G Memory 54000000 2 × 54000000 3.3 × 54000000 DIV area 20000000 2 × 20000000 3 × 20000000 MEQ Ctr 7000000 1.5 × 7000000 2 × 7000000 Q Mem Ctr 4000000 1.5 × 4000000 2 × 4000000 R Mem Ctr 2700000 1.5 × 2700000 2 × 2700000 Total 316000000 5500000000 10060000000 (MEQ=Matrix Equalizer; Mem=Memory; Ctr=Control) Based on this area comparison analysis, there is approximately a 75% increase in area from 2×3 to 3×3, and approximately a 300% increase in area from 2×3 to 4×4.
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No. 11/316,522, filed Dec. 21, 2009, to be published by USPTO, 109 pages.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8412257 *Jun 16, 2011Apr 2, 2013Qualcomm IncorporatedTransmit format selection with consideration for resource reuseUS9001873 *Apr 15, 2014Apr 7, 2015Marvell World Trade Ltd.Method and apparatus for recursively computing equalizer parameters for multiple-input multiple-output (MIMO) wireless communication channelsUS9014249 *Nov 2, 2012Apr 21, 2015Harris CorporationCommunications receiver with channel identification using A-priori generated gain vectors and associated methodsUS20140126621 *Nov 2, 2012May 8, 2014Harris CorporationCommunications receiver with channel identification using a-priori generated gain vectors and associated methodsUS20140226703 *Apr 15, 2014Aug 14, 2014Marvell World Trade Ltd.Method and Apparatus for Recursively Computing Equalizer Parameters for Multiple-Input Multiple-Output (MIMO) Wireless Communication ChannelsWO2013163629A1 *Apr 26, 2013Oct 31, 2013Propagation Research Associates, Inc.Method and system for using orthogonal space projections to mitigate interference* Cited by examinerClassifications U.S. Classification375/232, 375/349, 708/320, 375/231, 375/350, 375/260, 708/323International ClassificationH03H7/40, H03K5/159, H03H7/30Cooperative ClassificationH04B7/0413, H04L25/0204, H04L25/03159, H04B7/0854, H04L25/0246, H04L2025/03426, H04L2025/03414European ClassificationH04B7/04M, H04B7/08C4J2, H04L25/02C1, H04L25/02C11A3, H04L25/03B3Legal EventsDateCodeEventDescriptionSep 8, 2005ASAssignmentOwner name: MARVELL INTERNATIONAL LTD., BERMUDAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MARVELL SEMICONDUCTOR, INC.;REEL/FRAME:016979/0731Effective date: 20050906Owner name: MARVELL SEMICONDUCTOR, INC., CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SARRIGEORGIDIS, KONSTANTINOS;REEL/FRAME:016974/0893Effective date: 20050906Jan 12, 2015FPAYFee paymentYear of fee payment: 4RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services