Pulse or digital communications – Equalizers – Automatic
Reexamination Certificate
1999-11-24
2003-07-08
Chin, Stephen (Department: 2634)
Pulse or digital communications
Equalizers
Automatic
C375S284000, C375S285000, C375S348000, C375S350000, C708S322000, C329S320000, C329S353000
Reexamination Certificate
active
06590932
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 interference (noise). Fading effects include flat fading which may arise from the interaction of a transmitted signal (the main ray) with reflected versions of the transmitted signal that arrive concurrently at a receiver. Time dispersion, another type of fading, may arise from interaction of the main ray with time-delayed reflections of the main ray. Interference effects may be caused by interaction of non-orthogonal signals generated in the signal medium by sources other than the source of the desired transmitted signal. These various sources of signal disturbances may come from a variety of sources including thermal noise, a co-channel interferer and an adjacent-channel interferer. Most cellular communication standards typically require the receiver to achieve a minimum adjacent-channel protection (ACP). Unfortunately, to meet this minimum specification, a narrow receive filter is often used in the receiver at the expense of losing co-channel performance which might otherwise be obtainable with a wider receive filter.
The dynamic characteristics of the radio channel present difficulties in tracking 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 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 Gaussian (white) 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 over each data burst, an associated channel tracker is typically provided for the demodulator. The channel tracker typically operates in a “decision directed” mode where the symbol estimates are used to track the variations of the channel. After acquisition of a communicated signal by the receiver, the channel tracker maintains a channel estimate to provide a coherent reference between the demodulator and the received signal. The most commonly used channel tracking methods are the Least Mean Square (LMS) and Recursive Least Square (RLS) based algorithms. See for example, “Optimal Tracking of Time-varying Channels: A Frequency Domain Approach for known and new algorithms,”
IEEE Transactions on Selected Areas in Communications
, Vol. 13, NO. 1, January 1995, Jingdong Lin, John G. Proakis, Fuyun Ling.
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 filter
105
having a transfer function h(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
)
=
∑
i
=
0
L
-
1
c
(
i
)
s
(
n
-
i
)
+
w
(
n
)
,
(
1
)
where c(i) is the discrete-time based-band channel model with L coefficients, s is the transmitted symbols, and w(n) is a discrete-time random process caused by the signal disturbance (this sequence can be either colored or white and may be referred to as noise).
At a time “n ”, each state in the trellis of a maximum likelihood sequence estimation (MLSE) equalizer can be expressed as S
n
=[s(n),s(n−1), . . . , s(n−L+2)]. At each state of the trellis, a surviving path and a cumulative path metric M(S
n
) are kept for each of the 8
L−1
states. Also, at each stage of the trellis, the branch metric is:
dM
(
S
n
,
S
n
-
1
)
=
&LeftBracketingBar;
r
(
n
)
-
∑
i
=
0
L
-
1
c
(
i
)
s
^
(
n
-
1
)
&RightBracketingBar;
2
(
2
)
where ŝ is the signal (symbol) estimate and dM(S
n
,S
n−1
)corresponding to the state transition from one previous hypothesized state, S
n−1
, to the current hypothesized state, S
n
, which is computed and added to the path metric M(S
n−1
) associated with the previous state. The path metric of the current state may then be updated by choosing the minimum of the accumulated metrics among all paths that terminate in the current hypothesized state, S
n
.
Equation (2) implicitly assumes that w(n) is a white Gaussian sequence (i.e. it assumes that the w(n)s are uncorrelated in time). However, in many practical cases where the dominant disturbance is not the thermal noise, this assumption is not valid. Even when the disturbance is just the thermal noise, w(n) may not be white if the receive filter
105
h(t) is not Nyquist. In this case, however, the autocorrelation of w(n) denoted by &rgr;
ww
(m), is typically fixed and can be found by:
&rgr;
ww
(
m
)=
E[w
(
n
)
w
*(
n−m
)]=
N
0
∫h
(
t
)
h
*(
t−mT
)
dt,
(3)
where N
0
=E[|n(t)|
2
].
Typically, given any colored stationary sequence v(n), one can design a casual, invertible, linear and time-invariant (LTI) whitening filter with input v(n) and output z(n), where z(n) is white. As the whitening filter is generally causal and invertible, this filter typically does not cause any loss in information. This whitening filter is closely related to the linear least-squares one-step predictor of v(n). Specifically, let
w
^
(
n
|
n
-
1
)
=
∑
i
=
1
&infi
Hui Dennis
Ramesh Rajaram
Zangi Kambiz C.
Chin Stephen
Ericsson Inc.
Ha Dac V.
Myers Bigel & Sibley & Sajovec
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