US2013170587A1PendingUtilityA1
Systems and Methods for N-Dimensional Leaf-Node Prediction for MIMO Detection
Est. expiryMar 27, 2027(~0.7 yrs left)· nominal 20-yr term from priority
H04L 25/0204H04L 25/0242H04B 7/0413H04L 25/0246
44
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Claims
Abstract
Systems comprising a leaf node predictor for receiving a processed communications stream, determining at least one channel metric corresponding to the communications stream for a given channel realization, and generating at least three instructions to output, which at least one instruction corresponds to at least one predicted best leaf node candidate for the given channel realization.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 - 42 . (canceled)
43 . A multiple-input multiple-output (MIMO) detection system, comprising:
a leaf node predictor for receiving a processed communications stream, determining at least one channel metric, which is a function of a matrix, corresponding to the communications stream for a given channel realization, and generating at least three instructions to output including at least one vector, wherein the leaf node predictor does not have a channel output as an input.
44 . The system of claim 43 , wherein the leaf node predictor uses a two-dimension leaf-node predictor recursively at least N−1 times, where N is a number of symbols in a leaf-node candidate, wherein the dimension refers to the number of transmitted symbols.
45 . The system of claim 43 , wherein the leaf node predictor uses a look-up table listing instructions corresponding to best leaf node candidates for a given channel metric.
46 . The system of claim 43 , wherein the two-dimension leaf node predictor generates the instructions corresponding to best leaf node candidates for a given channel metric analytically without an instruction look-up table.
47 . The system of claim 43 , further comprising a MIMO engine.
48 . The system of claim 43 , wherein the leaf node predictor generates at least one instruction to output by optimizing a probability that the output contains at least a suitable approximation of a channel input with lowest cost.
49 . The system of claim 43 , further comprising a MIMO engine for enumerating at least one candidate vector corresponding to the at least one best leaf node candidate.
50 . The system of claim 49 , wherein interference cancellation is used to enumerate the at least one candidate vector.
51 . The system of claim 49 , wherein the MIMO engine computes the cost of each leaf node candidate.
52 . The system of claim 49 , further comprising a decoder for computing log-likelihood ratio values from the at least one candidate vector.
53 . The system of claim 43 , further comprising a wireless receiver for receiving a stream of information.
54 . The system of claim 43 , wherein the leaf node predictor further performs at least one of the following decompositions prior to calculating the channel metric: QR decomposition, Cholesky decomposition, and singular-value decomposition.
55 . The system of claim 43 , wherein the leaf node predictor determines the at least one channel metric, m i , such that
m
i
=
(
R
i
,
i
q
min
k
=
i
+
1
…
N
(
R
k
,
k
q
)
)
p
,
where R is an N×N triangular matrix with positive and real diagonals, and where q and p are non-zero real numbers.
56 . The system of claim 43 , wherein the leaf node predictor determines the at least one channel metric, m i , such that
m
i
=
(
h
~
i
q
min
k
=
i
+
1
…
N
(
h
~
k
q
)
)
p
,
where {tilde over (h)} k is the k-th column of HΠ, H is an M×N channel matrix, H is an N×N permutation matrix, and where q and p are non-zero real numbers.
57 . The system of claim 43 , wherein at least two instructions of the at least three instructions are generated by inference from the remaining at least one instruction.
58 . A multiple-input multiple-output (MIMO) detector, comprising:
a leaf-node predictor which generates, without using an instruction look-up table, at least three instructions directly from a channel metric which is a function of a matrix, and outputs the at least three instructions to a MIMO engine including at least one vector, wherein the leaf node predictor does not have a channel output as an input.
59 . The system of claim 58 , wherein at least two instructions of the at least three instructions are generated by inference from the remaining at least one instruction.
60 . The system of claim 58 , wherein the leaf node predictor determines the at least one channel metric, m i , such that
m
i
=
(
R
i
,
i
q
min
k
=
i
+
1
…
N
(
R
k
,
k
q
)
)
p
,
where R is an N×N triangular matrix with positive and real diagonals, and where q and p are non-zero real numbers.
61 . The system of claim 58 , wherein the leaf node predictor determines the at least one channel metric, m i , such that
m
i
=
(
h
~
i
q
min
k
=
i
+
1
…
N
(
h
~
k
q
)
)
p
,
where {tilde over (h)} k is the k-th column of HΠ, H is an M×N channel matrix, Π is an N×N permutation matrix, and where q and p are non-zero real numbers.
62 . The system of claim 58 , wherein the leaf node predictor uses a two-dimension leaf-node predictor recursively at least N−1 times, where N is a number of symbols in a leaf-node candidate, wherein the dimension refers to the number of transmitted symbols.
63 . The system of claim 58 , wherein the leaf node predictor further calculates at least one of the following decompositions prior to calculating the channel metric: QR decomposition, Cholesky decomposition, and singular-value decomposition.
64 . The system of claim 43 wherein:
a first instruction corresponds to at least one predicted best leaf node candidate for the given channel realization;
a second instruction is a vector of numbers; and
a 3rd instruction is a set of vectors.
65 . The system of claim 58 wherein:
a first instruction corresponds to at least one predicted best leaf node candidate for the given channel realization;
a second instruction is a vector of numbers; and
a 3rd instruction is a set of vectors.Cited by (0)
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