Pulse or digital communications – Spread spectrum – Direct sequence
Reexamination Certificate
2000-08-10
2003-02-18
Bocure, Tesfaldet (Department: 2631)
Pulse or digital communications
Spread spectrum
Direct sequence
C375S145000, C375S146000
Reexamination Certificate
active
06522683
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to an equalization technique in communication systems and more particularly to adaptive equalization in wireless data communication systems.
2. Description of Related Art
In relatively noise-free data communication systems, when data is transmitted over a communication channel by means of a linear modulation scheme, for example by using Quadrature Phase Shift Keying (“QPSK”), the number of detectable levels that the channel can support is essentially limited by Inter Symbol Interference (“ISI”). ISI arises because of the “spreading” of a transmitted symbol pulse due to the dispersive nature of the channel, which results in an overlap of adjacent symbol pulses. Stated differently, ISI occurs when a portion of a signal representative of one bit of information interferes with a different portion of the signal representative of a different bit of information.
The adverse effects of ISI are more pronounced where the signal to noise ratio is high and the channel is relatively noise-free. In such channels, which are typically present in data (as opposed to voice) communications, the presence of ISI greatly degrades performance of the communications system.
A common cause of ISI is the “multipath” phenomenon. Simply stated, multipath refers to interference caused by the reception of the same signal over multiple paths. Depending on the speed of a mobile wireless unit (also called the “subscriber unit”), condition of the surrounding environment such as existence of buildings or mountains, and the transmission bandwidth, the transmitted symbol pulses may arrive at the receiver at different times. As such, components of neighboring symbol pulses may interfere constructively or destructively.
However, even in the absence of “multipath,” some ISI may still be generated due to the imperfections in the transmit and receive filters employed in the communications system. For example, frequency dependent attenuations in the physical devices comprising the transmit and receive filters can be a source of
It is generally known that equalization can be used to minimize the effects of ISI. Equalization involves altering a signal so that it may be more easily recognized at the receiver. A signal may be altered at the transmitter so that the influence of the channel on the signal will yield a signal capable of being properly recognized at the receiver. However, transmitter-based equalization is difficult since the transmitter must have a priori knowledge of the characteristics of the channel and any changes that may occur to the characteristics of the channel over time.
Equalization may also be performed at the receiver. Receiver-based equalization can use properties of the received signal to adjust equalization parameters. In wireless communications, since the mobile channel is random and time varying, equalizers must track the time varying characteristics of the mobile channel, and are thus called adaptive equalizers. Adaptive equalization attempts to apply a correct amount of equalization to the channel. In adaptive equalization, the equalizer coefficients are initially, or periodically, adjusted to “adapt” to the varying channel conditions. The general operating modes of an adaptive equalizer are the training and tracking modes. In the training mode, a known pilot symbol sequence is sent by the transmitter so that the receiver's adaptive equalizer may average its coefficients to proper initial values. The training sequence, i.e. the pilot symbol sequence, is typically a fixed, prescribed bit pattern.
Immediately following the training sequence, the modulated and spread message data are sent, and the adaptive equalizer at the receiver uses a prescribed algorithm, such as “least mean square” (LMS) or “recursive least squares” (RLS), to estimate the adaptive equalizer coefficients in order to compensate for the ISI caused by the transmit filter, the communication channel, and the receive filter.
Each data frame sent from the transmitter contains an initial pilot sequence as well as a subsequent user message data sequence. As an example, the initial pilot sequence may take up approximately 5% of the entire data frame. However, the characteristics of the transmit filter, the communication channel, and the receive filter may change during each data frame, and also from frame to frame. Thus, the initial pilot sequence in each frame of data is used for achieving an initial setting of the equalizer coefficients. Moreover, after the initial setting of the equalizer coefficients, during each frame when user message data are received, the equalizer coefficients are adapted by utilizing a prescribed algorithm.
The initial pilot (or training) sequence in each frame is designed to permit the adaptive equalizer at the receiver to acquire the proper coefficients so that when the training sequence is finished, the filter coefficients are near optimal values for reception of user message data during the remainder of the frame. As user message data are received, the adaptive algorithm of the equalizer tracks the changing characteristics of the transmit filter, the communication channel, and the receive filter. As a consequence, the adaptive equalizer is continually changing its filter characteristics over time.
A common type of adaptive equalizer is a linear adaptive equalizer. There are two general types of linear adaptive equalizers, the “transversal” type and the “lattice” type.
FIG. 1
illustrates a transversal linear adaptive equalizer
100
. The transversal linear adaptive equalizer
100
in
FIG. 1
is a type of finite-duration impulse response filter (“FIR”) which is well known in the art. Referring to
FIG. 1
, an output of receive FIR filter
102
provides input to equalizer
100
which is marked as x(n) in FIG.
1
. The output of receive FIR filter
102
is coupled to unit delay element z
−1
106
and also to multiplier
104
which has a tap weight w
0
(n). The output of unit delay element z
−1
106
is marked as x(n−1) and is coupled to unit delay element z
−1
112
and to multiplier
108
which has a tap weight w
1
(n). The output of unit delay element z
−1
112
is marked as x(n−2) and is coupled to a subsequent unit delay element not shown in FIG.
1
and to multiplier
114
which has a tap weight w
2
(n). Unit delay element z
−1
122
and multiplier
118
having a tap weight of w
m−2
(n) receive their respective inputs, marked as x(n−m+2), from a previous unit delay element not shown in FIG.
1
. The output of unit delay element z
−1
122
is marked as x(n−m+1) and is coupled to multiplier
124
which has a tap weight w
m−1
(n).
Respective outputs of multipliers
104
,
108
,
114
,
118
, and
124
are added by adders
110
,
116
,
120
, and
126
to result in a final output â(n) of equalizer
100
. Equalizer output â(n) is fed to slicer
128
which results in a slicer output ã(n). Equalizer
100
output ã(n) is subtracted from slicer
128
output ã(n) by adder
130
. The result of the subtraction operation is e(n) which is the output of adder
130
.
By way of overview, during operation of equalizer
100
an adaptive algorithm is utilized to adapt the equalizer coefficients which are represented by tap weights w
0
(n), w
1
(n) to w
m−1
(n) in FIG.
1
. The equalizer coefficients are adjusted either on a sample by sample basis (i.e. whenever n is incremented by 1) or on a block by block basis (i.e. whenever a specified number of samples have been clocked into the equalizer). The adaptive algorithm used to adapt tap weights w
0
(n), w
1
(n) to w
m−1
(n) is controlled by the error signal e(n). During the tracking mode of the adaptive equalizer operation, the error signal e(n) is obtained by comparing the output â(n) of equalizer
100
with the output ã(n) of slicer
128
.
Slicer
128
is an example of a “decision making device” which applies a thresholding operation in order to arrive at a “hard estimate” of th
Jalali Ahmad
Ling Fuyun
Smee John E.
Wallace Mark
Baker Kent D.
Bocure Tesfaldet
QUALCOMM Incorporated
Wadsworth Philip R.
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