Pulse or digital communications – Receivers – Interference or noise reduction
Reexamination Certificate
2000-02-15
2004-01-06
Tran, Khai (Department: 2731)
Pulse or digital communications
Receivers
Interference or noise reduction
Reexamination Certificate
active
06674820
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to communications methods and apparatus, and more particularly, to methods and apparatus for receiving communications signals subject to noise such as those typically found in wireless communication systems. Wireless communications systems are commonly employed to provide voice and data communications to subscribers. For example, analog cellular radiotelephone systems, such as those designated AMPS, ETACS, NMT-450, and NMT-900, have long been deployed successfully throughout the world. Digital cellular radiotelephone systems such as those conforming to the North American standard IS-54 and the European standard GSM have been in service since the early 1990's. More recently, a wide variety of wireless digital services broadly labeled as PCS (Personal Communications Services) have been introduced, including advanced digital cellular systems conforming to standards such as IS-136 and IS-95, lower-power systems such as DECT (Digital Enhanced Cordless Telephone) and data communications services such as CDPD (Cellular Digital Packet Data). These and other systems are described in
The Mobile Communications Handbook
, edited by Gibson and published by CRC Press (1996).
Wireless communications systems such as cellular radiotelephone systems typically include a plurality of communication channels which may be established between a first transceiver (such as a base station) and a second transceiver (such as a mobile terminal). The communication channels typically are subject to performance-degrading environmental effects such as multi-path fading and additive disturbances. These various sources of additive disturbances may come from a variety of sources including thermal noise, a co-channel interferer and an adjacent-channel interferer.
The dynamic characteristics of the radio channel present difficulties in estimating the channel to allow for decoding of information contained in the received signal. Often, in wireless mobile radio systems, known data sequences are inserted periodically into the transmitted information sequences. Such data sequences are commonly called synchronizing sequences or training sequences and are typically provided at the beginning and/or in the middle of a frame of data or a burst of data. Channel estimation may be carried out using the synchronizing sequences and other known parameters to estimate the impact the channel has on the transmitted signal. Least square estimation may be an efficient way of estimating the channel impulse response in the presence of additive white Gaussian noise. However, as the noise becomes non-white, or colored, these techniques may become less effective.
To extract the transmitted signal (or symbols) from the received signal, the receiver of a mobile terminal typically includes a demodulator which may be a coherent demodulator such as a maximum likelihood sequence estimation (MLSE) demodulator (or equalizer). To adapt to the channel variation from each data burst to the next, an associated channel estimator is typically provided for the demodulator. The channel estimator typically operates using known transmitted symbols.
At any given time, the kind of disturbances (co-channel interferences, adjacent-channel interference, or thermal noise) that dominates in the received signal is generally unknown. The typical approach is to design the demodulator or the equalizer in the receiver assuming the dominant disturbance is white (i.e. uncorrelated in time), hoping that it will suffice well even when the disturbance is somewhat colored.
For example, consider the receiver model depicted in
FIG. 1. A
signal y(t) is first filtered in an analog receive filter
105
having a transfer function p(t) to provide a received signal r(t) which is downsampled to a symbol rate received signal r(n) before processing in the equalizer
110
to get a signal estimate s
est
(u). As used herein, the term “symbol rate” encompasses both the symbol transmission rate and multiples thereof. The symbol-rate downsampled discrete-time received signal r(n) is given by:
r
(
n
)
=
∑
k
=
0
L
-
1
c
(
k
)
s
(
n
-
k
)
+
v
(
n
)
(
1
)
where c(k) are the L coefficients of the baseband channel, s(n) are the transmitted symbols, and v(n) is a disturbance signal.
As noted above, to aid in estimating the channel c(k) at the receiver, the transmitter typically transmits a synchronization signal including a number of known symbols: {s(n)}
n=n0
n0+M−1
. The channel coefficients, c(k)'s, are then estimated using the known transmitted symbols {s(n)}
n=n0
n0+M−1
and the known received signal {r(n)}
n=n0
n0+M−1
. Generally, this is done by assuming that the disturbance v(n) is white, in other words, that the auto-correlation of v(n), &rgr;
vv
(k)=&dgr;(k). Based on this assumption, the maximum likelihood (ML) estimate, expected to be the optimal estimate, of the c(k)'s is the least-squares estimate.
The auto-correlation function of the disturbance v(n) may be defined as:
&rgr;
vv
(
k
)=
E{v
(
n
)
v
*(
n−k
)} (2)
where k is the auto-correlation lag and E{ } represents the expected value. It is known that the least-squares estimate may be obtained as the solution to the following optimization criteria:
c
^
LS
(
k
)
=
c
(
k
)
arg
min
∑
n
=
n0
+
L
n0
+
M
-
1
&LeftBracketingBar;
r
(
n
)
-
r
^
[
n
|
n
-
1
;
c
(
k
)
;
ρ
vv
(
k
)
=
δ
(
k
)
]
&RightBracketingBar;
2
(
3
)
=
c
(
k
)
arg
min
∑
n
=
n0
+
L
n0
+
M
-
1
&LeftBracketingBar;
r
(
n
)
-
∑
k
=
0
L
-
1
c
(
k
)
s
(
n
-
k
)
&RightBracketingBar;
2
(
4
)
where {circumflex over (r)}[n|n−1;c(k);&rgr;
vv
(k)] is the one-step ahead prediction of r(n) given {r(k):k<n}, {s(k):k≦n} and the channel coefficients c(k). It is further based on the assumption that the signal disturbance is white noise, in other words, that the auto-correlation of the disturbance &rgr;
vv
(k)=&dgr;(k). When the noise v(n) is not white (i.e. &rgr;
vv
(k)≠&dgr;(k)), the least-squares estimate defined in equation (4) is not expected to be the maximum likelihood (ML) estimate of c(k).
In a typical cellular system, the disturbance v(n) can be modeled as the sum of three signals passed through the analog receive filter p(t):
v
(
t
)=[v
co
(
t
)+
v
adj
(
t
)+
v
TH
(
t
)]*
p
(
t
) (5)
v
(
n
)=
v
(
n×T
symbol
), (6)
where v
co
(t) is the analog co-channel interferer before the receive filter; v
adj
(t) is the analog adjacent channel interferer before the receive filter; v
TH
(t) is the thermal noise before the receive filter; and p(t) is the analog receive filter. Finally, v(n) is obtained by sampling v(t) every T
symbol
seconds.
Note that v(n) might become colored because v
co
(t) or v
adj
(t) can be colored. Moreover, v(n) might become colored because p(t) is not a Nyquist filter. In other words, the signal disturbance v(n) may become colored and the color of the disturbance may change from burst to burst of the communications signal. A colored signal disturbance may result in degraded performance because, as noted above, once the disturbance is colored, the ML estimate of the channel coefficients is typically not the least-squares estimate defined in equation (4).
SUMMARY OF THE INVENTION
According to embodiments of the present invention, methods, systems and receiver devices are provided which may provide improved receiver performance in obtaining estimates of the complex-valued baseband cha
Hui Dennis
Ramesh Rajarem
Zangi Kambiz C.
Ericsson Inc.
Myers Bigel & Sibley & Sajovec
Tran Khai
LandOfFree
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