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Grassmannian Packings for Efficient Quantization in MIMO Broadcast Transmission. MIMO Broadcast Transmission Examples GRM(m) for MIMO Broadcast Systems transmission to mobiles with orthogonal channel vectors transmission to mobiles with almost orthogonal channel vectors - PowerPoint PPT Presentation
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Grassmannian Packings for Efficient Quantization in MIMO Broadcast
Transmission
Alexei Ashikhmin and RaviKiran GopalanBell Labs Texas Instrument
MIMO Broadcast Transmission
Examples GRM(m) for MIMO Broadcast Systems • transmission to mobiles with orthogonal channel vectors • transmission to mobiles with almost orthogonal channel vectors
Simulation Results
Algebraic Construction of GRM(m)
MIMO Broadcast Transmission
Base
Station
is a quantization code
The Base Station (BS):
• chooses some mobiles, for example mobiles 1,2,3
• forms and using computes a precoding matrix
• transmits to mobiles 1,2,3 using the precoding matrix
Requirements for a quantization code
• should provide good quantization (for given size )
• should afford a simple decoding
• should have many sets of M orthogonal codewords (bases of )
BS
is the channel vector of
is the channel vector of
is the channel vector of
If are pairwise orthogonal then signals sent to do
not interfere with each other
• Mobiles quantize:
• Base Station strategy – among find orthogonal codewords, say , and transmit to the corresponding mobiles 1,3,5
• The channel vectors of these mobiles will be almost orthogonal
Base
Station
If a channel vector is quantized into we say that is occupied
and mark by
• If the number of mobiles (channel vectors) is large, e.g. , then
with a high probability all codewords will be occupied
• In this case even if we have only a few sets of orthogonal codewords, we easily find a set of occupied orthogonal codewords
Let us have a quantization code
orthogonal codewords
• The number of mobiles is small, say
• Still if there are many sets of orthogonal codewords, there is a chance to find occupied orthogonal codewords
• For example, let
be sets of orthogonal codewords. Then
Example:
The number of antennas
The first code in the family:
(for practical applications we
add four vectors to the code
to make the code size 64)
105 orthogonal bases
(1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)
(1, 0, 1, 0), (0, 1, 0, 1), (1, 0, -1, 0), (0, -1, 0, 1)
(1, 0, -i, 0), (0, 1, 0, -i), (1, 0, i, 0), (0, 1, 0, i)
(1, 1, 0, 0), (0, 0, 1, 1), (1, -1, 0, 0), (0, 0, -1, 1)
(1, -i, 0, 0), (0, 0, 1, -i), (1, i, 0, 0), (0, 0, 1, i)
(1, 0, 0, 1), (0, 1, 1, 0), (1, 0, 0, -1), (0, 1, -1, 0)
(1, 0, 0, -i), (0, 1, i, 0), (1, 0, 0, i), (0, 1, -i, 0)
(1, 1, 1, 1), (1, -1, 1, -1), (1, 1, -1, -1), (1, -1, -1, 1)
(1, 1, -i, -i), (1, -1, -i, i), (1, 1, i, i), (1, -1, i, -i)
(1, -i, 1, -i), (1, i, 1, i), (1, -i, -1, i), (1, i, -1, -i)
(1, -i, -i, -1), (1, i, -i, 1), (1, -i, i, 1), (1, i, i, -1)
(1, -i, -i, 1), (1, i, -i, -1), (1, -i, i, -1), (1, i, i, 1)
(1, -i, 1, i), (1, i, 1, -i), (1, -i, -1, -i), (1, i, -1, i)
(1, 1, 1, -1), (1, -1, 1, 1), (1, 1, -1, 1), (1,-1,-1,-1)
(1, 1,-i, i), (1, -1, -i, -i), (1, 1, i, -i), (1, -1, i, i)
The number of mobiles
• The bases form the constant weight code (n=60, |C|=105, w=4).
• With probability 0.65 will find four orthogonal occupied codewords
• With probability 0.349 will find three orthogonal occupied codewords
Examples (continued)
1.
The number of orthogonal bases is 105. Each codeword belongs to
7 bases. The bases form the constant weight code (n=60, |C|=105, w=4).
2.
The number of orthogonal bases is 1076625. Each codeword belongs to
7975 bases. The bases form the constant weight code
(n=1080, |C|=1076625, w=8)
If K is small that the probability to find M occupied orthogonal codewords is
also small
What to do? - Use almost orthogonal codewords
Def. Orthonormal bases of are mutually unbiased
if for any we have
Theorem The number of MUBs
Def. (i.e. ) is a full size MUB set.
Mutually Unbiased Bases (MUB)
Bases form a full size MUB set
• MUB sets form a constant weight code C (n=15, |C|=6, w=5)
• If K is small the chance that M occupied codewords are covered by
an MUB set is significantly higher than that they are covered by a basis
There are 840 full size MUB sets , each belongs to 56
full size MUB sets
Simulation Results
All results for M=8, i.e. the number of Base Station antennas is 8
K=1000
GRM(3)
Yoo and Goldsmith greedy alg. with RVQ
RVQ with Reg. ZF
RVQ with ZF
GRM(3)
If K=50 typically
we can find 5 or 6
occupied codewords
GRM(3),
GRM(3),
GRM(3)
greedy alg.
Transmission to
Transmission to
are orthogonal
are orthogonaland
GRM(m) is a code in
There are two methods for construction of GRM(m):
1. Group theoretic approach – a particular case of the Operator Reed-Muller codes (A.Ashikhmin and A.R.Calderbank, ISIT 2005)
2. Coding theory approach
Construction of GRM(m)
Pauli matrices:
Group Theoretic Construction of GRM(m)
where
Def. Vectors and are orthogonal (with respect
to the symplectic inner product) if
Construction of GRM(m)
• is a set of orthogonal independent vectors
• .
Lemma 2 The operator is an orthogonal projector on a subspace ,
GRM(m) is obtained by merging of
1. Binary Reed-Muller codes RM(r,m)
2. Reed-Muller codes ZRM(2,m) codes over
ZRM(2,m) is generated by the Boolean functions:
Coding Theory approach for construction of GRM(m)
ZRM(2,m) is generated by the Boolean functions
Let us construct the code from ZRM(2,m) by mapping
For example
Codewords of GRM(2):
Merging RM(r,2) and CRM(2,2) into GRM(2)
(1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)
r changes from m=2 to 0:
(1,i)
(1,-i)
(1,1)
(1,-1)
Minimum weight codeword of RM(1,2):
(1,1,0,0)
(1,i,0,0)
(1,-i,0,0)
(1,1,0,0)
(1,-1,0,0)
(0,1,i,0)
(0,1,-i,0)
(0,1,1,0)
(0,1, -1,0)
(0,1,1,0)
3. r=m-2=0: take the only minimum weight codeword of RM(r,m)=RM(0,m):
(1,1,1,1) and substitute into its nonzero positions codewords of
1. r=m=2: take the all minimum weight codewords of RM(r,m)=RM(2,2):
2. r=m-1=1: substitute codewords of
into the minimum weight codewords of RM(r,m)=RM(1,2)
(1,0,0,0),(0,1,0,0),(0,0,1,0),(0,0,0,1)
(1,1,0,0),(1,i,0,0),(1,-1,0,0),(1,-i,0,0)
(1,0,1,0),(1,0,i,0),(1,0,-1,0),(0,1,0,-i)
(1,0,0,1),(1,0,0,i),(1,0,0,-1),(1,0,0,-i)
(0,1,1,0),(0,1,i,0),(0,1,-1,0),(0,1,-i,0)
(0,1,0,1),(0,1,0,i),(0,1,0,-1),(1,0,-i,0)
(0,0,1,1),(0,0,1,i),(0,0,-1,1),(0,0,1,-i)
(1,1,1,1), (1,-1,1,-1), (1,1,-1,-1), (1,-1,-1,1),
(1,1,-i,-i), (1,-1,-i,i), (1,1,i,i), (1,-1,i,-i),
(1,-i,1,-i), (1,i,1,i), (1,-i,-1,i), (1,i,-1,-i),
(1,-i,-i,-1), (1,i,-i,1), (1,-i,i,1), (1,i,i,-1),
(1,-i,-i,1), (1,i,-i,-1), (1,-i,i,-1),(1,i,i,1),
(1,-i,1,i), (1,i,1,-i), (1,-i,-1,-i), (1,i,-1,i),
(1,1,1,-1), (1,-1,1,1), (1,1,-1,1), (1,-1,-1,-1),
(1,1,-i,i), (1,-1,-i,-i), (1,1,i,-i), (1,-1,i,i)
r=0, minimum weights v codewords of RM(2,2)
r=1, minimum weights v codewords of RM(1,2) v +codewords of CRM(2,1)
r=2, minimum weights v codewords of RM(0,2) v +codewords of CRM(2,2)
Theorem (Inner product distribution of GRM(m)). For any
we have
and the number of such that is
Theorem
Example:
Example: in GRM(2) there are 15 vectors such that
in GRM(3) there are 315 vectors such that
Theorem The maximum root-mean-square (RMS) inner product is
Theorem For any basis there exist bases
such that is an MUB set.
Decoding
GRM(3), |GRM(3)|=1080 Random Code C, |C|=1080
• Complex
multiplications 0 8*1080
• Complex
summations 1500 7*1080
Example M=8
Mobiles quantize:
If some channel vector is quantized into we say that is occupied
The number of mobiles
is large, say
In this case, even if we
have only one set of
orthogonal vectors, say
we are doing
fine
Example: The number of BS antennas M=4, hence
(1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)
(1, 0, 1, 0), (0, 1, 0, 1), (1, 0, -1, 0), (0, -1, 0, 1)
(1, 0, -i, 0), (0, 1, 0, -i), (1, 0, i, 0), (0, 1, 0, i)
(1, 1, 0, 0), (0, 0, 1, 1), (1, -1, 0, 0), (0, 0, -1, 1)
(1, -i, 0, 0), (0, 0, 1, -i), (1, i, 0, 0), (0, 0, 1, i)
(1, 0, 0, 1), (0, 1, 1, 0), (1, 0, 0, -1), (0, 1, -1, 0)
(1, 0, 0, -i), (0, 1, i, 0), (1, 0, 0, i), (0, 1, -i, 0)
(1, 1, 1, 1), (1, -1, 1, -1), (1, 1, -1, -1), (1, -1, -1, 1)
(1, 1, -i, -i), (1, -1, -i, i), (1, 1, i, i), (1, -1, i, -i)
(1, -i, 1, -i), (1, i, 1, i), (1, -i, -1, i), (1, i, -1, -i)
(1, -i, -i, -1), (1, i, -i, 1), (1, -i, i, 1), (1, i, i, -1)
(1, -i, -i, 1), (1, i, -i, -1), (1, -i, i, -1), (1, i, i, 1)
(1, -i, 1, i), (1, i, 1, -i), (1, -i, -1, -i), (1, i, -1, i)
(1, 1, 1, -1), (1, -1, 1, 1), (1, 1, -1, 1), (1,-1,-1,-1)
(1, 1,-i, i), (1, -1, -i, -i), (1, 1, i, -i), (1, -1, i, i)
105 orthogonal
bases
Example: The number of BS antennas M=4, hence
(1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)
(1, 0, 1, 0), (0, 1, 0, 1), (1, 0, -1, 0), (0, -1, 0, 1)
(1, 0, -i, 0), (0, 1, 0, -i), (1, 0, i, 0), (0, 1, 0, i)
(1, 1, 0, 0), (0, 0, 1, 1), (1, -1, 0, 0), (0, 0, -1, 1)
(1, -i, 0, 0), (0, 0, 1, -i), (1, i, 0, 0), (0, 0, 1, i)
(1, 0, 0, 1), (0, 1, 1, 0), (1, 0, 0, -1), (0, 1, -1, 0)
(1, 0, 0, -i), (0, 1, i, 0), (1, 0, 0, i), (0, 1, -i, 0)
(1, 1, 1, 1), (1, -1, 1, -1), (1, 1, -1, -1), (1, -1, -1, 1)
(1, 1, -i, -i), (1, -1, -i, i), (1, 1, i, i), (1, -1, i, -i)
(1, -i, 1, -i), (1, i, 1, i), (1, -i, -1, i), (1, i, -1, -i)
(1, -i, -i, -1), (1, i, -i, 1), (1, -i, i, 1), (1, i, i, -1)
(1, -i, -i, 1), (1, i, -i, -1), (1, -i, i, -1), (1, i, i, 1)
(1, -i, 1, i), (1, i, 1, -i), (1, -i, -1, -i), (1, i, -1, i)
(1, 1, 1, -1), (1, -1, 1, 1), (1, 1, -1, 1), (1,-1,-1,-1)
(1, 1,-i, i), (1, -1, -i, -i), (1, 1, i, -i), (1, -1, i, i)
105 orthogonal
bases
Merging of RM(r,m) and CRM(2,m)
1. r=m=2: take the all minimum weight codewords of RM(r,2)=RM(2,2):
2. r=m-1=1: substitute codewords of
into minimum weight codewords of RM(r,2)=RM(1,2)
3. r=m-2=0: take the only minimum weight codeword of RM(r,m)=RM(0,m):
(1,1,1,1) and substitute into its nonzero positions codewords of
(1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 1)
changes from m to 0:
(1,i)
(1,-i)
(1,1)
(1,-1)
Minimum weight codeword of RM(1,2): Codewords of G-ZRM(2):
(1,1,0,0)
(1,i,0,0)
(1,-i,0,0)
(1,1,0,0)
(1,-1,0,0)
(0,1,i,0,0)
(0,1,-i,0)
(0,1,1,0)
(0,1, -1,0)
(0,1,1,0)
Lemma 1 The operator is an orthogonal projector,
Def. Vectors and are orthogonal (with respect
to the symplectic inner product) if
• is a set of orthogonal vectors
• .
Lemma 2 The operator is an orthogonal projector on a subspace
Def. Orthonormal bases of are mutually unbiased
if for any we have
Theorem The number of MUBs
Def. (i.e. ) is full size MUB set.
Mutually Unbiased Bases (MUB)
Bases form a full size MUB set