Pulse or digital communications – Receivers – Angle modulation
Reexamination Certificate
1999-06-10
2002-09-10
Le, Amanda T. (Department: 2634)
Pulse or digital communications
Receivers
Angle modulation
C375S341000, C329S304000
Reexamination Certificate
active
06449322
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates generally to the transmission and detection of digital data using analog signals, and more particularly the invention relates to the detection of phase shift keying (PSK) encoded digital data.
The phase of a carrier signal can be used to encode digital data for transmission. The number of bits represented by a carrier phase symbol depends on the number of phases M of the carrier in an MPSK data burst.
A prior art approach to the detection of data symbols consists of using a phase locked loop to lock to the reference symbols and then detecting the data symbols using the phase reference out of the loop. A related approach is to use both reference symbols and remodulated data symbols to obtain a loop phase reference. These approaches are well known.
Another approach is to form a phase reference using a filtering operation on the reference symbols, often called pilot symbol aided demodulation. This approach is essentially the same as the phase locked loop approach in the sense that the phase locked loop also performs a filtering operation.
The present invention is concerned with maximum likelihood detection of data symbols in an MPSK data burst with inserted reference symbols.
SUMMARY AND DESCRIPTION OF THE DRAWINGS
The present invention presents a fast algorithm to perform maximum likelihood detection of data symbols. The figures of the drawings (
FIGS. 1A
,
1
B,
2
,
3
A,
3
B) illustrate flow diagrams of four embodiments in implementing algorithm.
DETAILED DESCRIPTION OF THE INVENTION
First consider a specific problem which however has all the essential features of the general problem. Consider that N data symbols s
1
, s
2
, . . . s
N
are transmitted at times 1, 2, . . . N, and that a reference symbol s
N+1
is transmitted at time N+1. All N+1 symbols are MPSK symbols, that is, for k=1, . . . N, s
k
=e
j&phgr;k
, where &phgr;k is a uniformly distributed random phase taking values in {0,2&pgr;/M, . . . 2&pgr;(M−1)/M}, and for k=N+1, reference symbol s
N+1
is the MPSK symbol e
j0
=1. The N+1 symbols are transmitted over an AWGN (Additive white Gaussian noise) channel with unknown phase, modeled by the equation:
r=s
e
j&thgr;
+n.
(1)
where r, s, and n are N+1 length sequences whose k
th
components are r
k
, s
k
, and n
k
, respectively, k=1, . . . N+1. Further, n is the noise sequence of independent noise samples, r is the received sequence, and &thgr; is an unknown channel phase, assumed uniformly distributed on (−&pgr;,&pgr;].
We now give the maximum likelihood decision rule to recover the data s
1
, . . . s
N
. For the moment, first consider the problem where we want to recover s=s
1
, . . . s
N+1
, where s
N+1
is assumed to be unknown. We know that the maximum likelihood rule to recover s is the s which maximizes p(r|s). From previous work, we know that this is equivalent to finding the s which maximizes &eegr;(s), where:
η
(
s
)
=
&LeftBracketingBar;
∑
k
=
1
N
+
1
r
k
s
k
*
&RightBracketingBar;
2
.
(
2
)
In general, there are M solutions to (2). The M solutions only differ by the fact that any two solutions are a phase shift of one another by some multiple of 2&pgr;/M modulo 2&pgr;. Now return to the original problem which is to recover the data s
1
. . . s
N
. The maximum likelihood estimate of s
1
, . . . s
N
must be the first N components of the unique one of the M solutions of (2) whose s
N+1
component is e
j0
=1.
An algorithm to maximize (2) when all s
k
, k=1, . . . N+1 are unknown and differentially encoded is given in K. Mackenthun Jr., “A fast algorithm for multiple-symbol differential detection of MPSK”,
IEEE Trans. Commun.,
vol 42, no. 2/3/4, pp. 1471-1474, February/March/April 1994. Therefore to find the maximum likelihood estimate of s
1
, . . . s
N
when s
N+1
is a reference symbol, we only need to modify the algorithm for the case when s
N+1
is known.
The modified algorithm to find the maximum likelihood estimate ŝ
1
, . . . ŝ
N
of s
1
, . . . s
N
is as follows. Let &PHgr; be the phase vector &PHgr;=(&phgr;
1
, . . . &phgr;
N+1
), where all &phgr;
k
can take arbitrary values, including &phgr;
N+1
. If |r
k
|=0, arbitrary choice of s
k
will maximize (2). Therefore, we may assume with no loss in generality that |r
k
|>0, k=1, . . . N. For a complex number &ggr;, let arg[&ggr;] be the angle of &ggr;.
Let
=(
1
, . . .
N+1
) be the unique &PHgr; for which:
arg[
r
k
e
−j&psgr;
k
]&egr;[0,2&pgr;/
M
),
for k=1, . . . N+1. Define z
k
by:
z
k
=r
k
e
−j−{tilde over (&psgr;)}
k
. (3)
For each k, k=1, . . . N+1, calculate arg[z
k
]. List the values arg[z
k
] in order, from largest to smallest. Define the function k(i) as giving the subscript k of z
k
for the i
th
list position, i=1, . . . N+1. Thus, we have:
0
≤
arg
[
z
k
(
N
+
1
)
]
≤
arg
[
z
k
(
N
)
]
≤
…
≤
arg
[
z
k
(
1
)
]
<
2
π
M
.
(
4
)
For i=1, . . . N+1, let:
g
i
=zk
(
i
). (5)
For i satisfying N+1<i≦2(N+1), define:
g
i
=e
−j2&pgr;j2
g
i−(N+1)
. (6)
Calculate:
&LeftBracketingBar;
∑
i
=
q
q
+
N
g
i
&RightBracketingBar;
2
,
for
q
=
1
,
…
N
+
1
,
(
7
)
and select the largest.
Suppose the largest magnitude in (7) occurs for q=q′. We now find the phase vector
corresponding to q=q′. Using (3), (5), and (6), with i in the range of q′≦i≦q′+N, we have:
k(i)
=
k(i)
,q′≦i≦N+1 (8)
ϕ
~
~
k
(
i
-
N
)
=
ϕ
~
k
(
i
-
[
N
+
1
]
)
+
2
π
M
)
,
N
+
1
<
i
≤
q
′
+
N
.
(
9
)
The evaluation of (8) and (9) gives elements
k(l)
, 1=1, . . . N+1, in order of subscript value k(1), by arranging the elements
k(l)
, l=1, . . . N+1 in order of subscript value k(l), we form the sequence
1
,
2
, . . .
,
N+1
, which is the vector
. The maximum likelihood estimate of ŝ
1
, . . . ŝ
N
is now given by ŝ
k
e
j
k
, . . . k=1, . . . N, where
k
=
k
−
N+1
, k=1, . . . N.
As discussed in Mackenthun supra, algorithm complexity is essentially the complexity of sorting to obtain (4), which is (N+1)log(N+1) operations.
We now expand the specific problem considered earlier to a more general problem. Suppose that N data symbols are transmitted followed by L reference symbols s
N+1
, . . . s
N+L
, where s
k
=e
j0
=1 for k=N+1, . . . N+L, and assume the definition of channel model (1) is expanded so that r, s, and n are N+L length sequences. Then in place of (2) we have:
η
(
s
)
=
&LeftBracketingBar;
∑
k
=
1
N
+
L
r
k
s
k
*
&RightBracketingBar;
2
.
(
10
)
However, note that (10) can be rewritten as:
η
(
s
)
=
&LeftBracketingBar;
∑
k
=
1
N
r
k
s
k
*
+
r
N
+
1
′
s
N
+
1
*
&RightBracketingBar;
2
(
11
)
where r′
N+1
=r
N+1
+r
N+2
+ . . . r
N+L
. But we can apply the previous modified algorithm exactly to (11) and thereby obtain a maximum likelihood estimate of the first N data symbols.
Now suppose the L reference symbols can take values other than e
j0
. Since the reference symbols are known to the receiver, we can remodulate them to e
j0
and then obtain a result in the form (11), and apply the previous algorithm. Finally, suppose the L reference symbols are scattered throughout the data. It is clear that we can still obtain a result in the form (11) and apply the previous algorithm.
If desired, sorting can be avoid
Le Amanda T.
Stanford Telecommunications, Inc.
Townsend and Townsend / and Crew LLP
Woodward Henry K.
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