Pulse or digital communications – Equalizers – Automatic
Reexamination Certificate
2000-09-19
2004-07-06
Vo, Don N. (Department: 2631)
Pulse or digital communications
Equalizers
Automatic
C375S350000
Reexamination Certificate
active
06760374
ABSTRACT:
FIELD OF THE INVENTION
The present invention generally relates to an improved data communication system and technique, more particularly relates to an improved equalization scheme for a digital communication system, and even more particularly relates to the block decision feedback equalization of signals transmitted via a dynamic dispersive media.
BACKGROUND OF THE INVENTION
Equalization is a technique often used in wireless communication applications. For example, when digital data is transmitted over a dispersive communication channel, an equalizer can be used to improve the estimate of the originally transmitted symbols. More particularly, equalizers are becoming increasingly important in high frequency (HF) radio communication applications. Such applications are requiring increasingly higher data rates in a fixed bandwidth; i.e. increased bandwidth efficiency. For example, many current applications require performance sufficient to meet the specifications for a 64-QAM (quadrature-amplitude modulation) signal.
As bandwidth efficiency is increased, however, information reliability is typically reduced. The reduction in reliability results, at least in part, from various channel effects. Equalization techniques can be used to mitigate many of the various channel effects. Use of an equalization technique can thereby make the communication system more reliable.
Several different channel effects can cause a reduction in reliability. A description of some of the various channel effects follows. Constrained channel bandwidth, for example, can cause time dispersion and intersymbol interference (ISI). Thermal noise and interference can lower the signal-to-noise ratio and thereby impact performance. Multipath interference can cause fading and time dispersion. Fading can also result from the physical characteristics of the communication channel being used. Delay and Doppler shift, which also result from the physical characteristics of the communication channel being used, can cause, respectively, time and frequency shifts.
Transmitter and receiver characteristics, which also affect the signal, must be accounted for as well. For example, the transmitter can include transmit filters and automatic level control (ALC). Likewise, the receiver can include receive filters and automatic gain control (AGC). These system influences should be addressed since they are also included in the channel response of any real system.
The equalizer's role is to compensate for such effects.
FIG. 1
depicts a block diagram of a basic communication system. Data from a source
100
is fed to a modulator
102
that in turn passes the modulated data to a wireless communication component
104
. The communication signal is then transmitted by a transmitter antenna
106
to a receiver antenna
108
. The link between the two antennas
106
,
108
is known as the propagation channel
110
. The receiver antenna
108
passes the received signal to the receiver's wireless communication component
112
that in turn sends the signal to a receiver-based demodulator
114
. The demodulated signal is then sent for further processing, handling or storage as represented by sink
116
.
The channel between the two wireless communication components
104
,
112
is known as the radio channel
118
. The channel between the modulator
102
and the demodulator
114
is known as the modulation channel
120
. The channel between the source
100
and the sink
116
is known as the digital channel
122
. The equalizer typically compensates for the influences found across the modulation channel
120
. Thus, when used for such a purpose, it can be used to compensate for the channel and system effects described above.
FIG. 2
depicts a use of an equalizer as a filter compensating for the effects influencing the modulation channel. The effects of the transmitter
200
can be represented as H
Tx
(f). The effects of the propagation channel
202
can be represented as C(f). The effects of the receiver
204
can be represented as H
Rx
(f). For example, the ideal response of a linear equalizer
206
can be represented as a filter that mitigates the effects of the modulation channel by having a response such as [H
Tx
(f)C(f)H
Rx
(f)]
−1
.
Typical equalizer implementations require knowledge of the characteristics of the communication channel. Several different techniques have been used to estimate or model these characteristics. For example, the initial channel characteristics can be estimated using a training sequence. The estimate can then be updated using a least-mean squares (LMS) algorithm or a recursive-least squares (RLS) algorithm. Another technique employs periodically inserted correlator probes, which are used to guide the channel updates. Further, use of a carefully designed probe can enable an efficient estimation of the channel characteristics. One example of such a probe is provided in the MIL-STD-188-110B Appendix C standard, wherein a simple correlator can be used to obtain the channel estimate.
An example of a typical signaling sequence is depicted in FIG.
3
. An initial preamble
300
of “x” symbols is used for acquisition and synchronization. In this example, the data packets
302
,
304
each include thirty-two unknown symbols. The probes
306
,
308
each include a sequence of sixteen known symbols. Alternatively, different numbers of symbols can be communicated in the unknown data segments
302
,
304
and probes
306
,
308
. For example, in the more recently developed MIL-STD-188-110B Appendix C standard, each known probe segment includes thirty-one known symbols and each unknown data segment
302
,
304
includes 256 unknown symbols. Each “unknown” data segment
302
,
304
is thus encapsulated between two “known” probe packets
300
,
306
,
308
. Various modulation techniques, such as phase-shift keying (PSK), quadrature amplitude modulation (QAM) and quadrature phase-shift keying (QPSK), for example, can be used to transmit the various segments of the signaling sequence.
An example of a communication system using an encapsulated unknown data/known probe approach, such as that depicted in
FIG. 3
, can be found in U.S. Pat. No. 4,365,338 (Technique For High Rate Digital Transmission Over A Dynamic Dispersive Channel) to McRae et al. The technique disclosed in U.S. Pat. No. 4,365,338, however, uses a normal equations approach to solve the over-determined least-squares problem. New methods and approaches are needed for determining the symbols of the unknown data segment in environments wherein the channel characteristics can vary over the duration of the unknown data segment.
Consequently, there exists a need for equalization approaches capable of taking changing characteristics into consideration when determining symbols transmitted in an unknown data segment. These needs and others are fulfilled by the invention disclosed in the following detailed description.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide an equalizer for a communication system transmitting data via a dispersive media.
It is one feature of the present invention to utilize QR factorization to solve the over-determined least-squares problem.
It is an advantage of the present invention to provide an accurate and reliable determination of the symbols of the unknown data segment even in situations wherein the channel characteristics vary over the duration of the unknown data segment.
Thus, the present invention involves an improved block equalization apparatus and method that relies on a QR factorization approach to the least-squares detection problem, rather than the normal equations.
REFERENCES:
patent: 4365338 (1982-12-01), McRae et al.
patent: 5436929 (1995-07-01), Kaleh
patent: 5630154 (1997-05-01), Bolstad et al.
patent: 5790598 (1998-08-01), Moreland et al.
patent: 6061396 (2000-05-01), Everitt
Liu, Zheng-She, QR Methods of O(N) Complexity in Adaptive Parameter Estimation, Mar. 1995, IEE Transaction on Signal Processing, vol. 43, No. 3, pp 720-729.*
F.M. Hsu, “Data Directed Estimation Techniqu
Graf Joseph T.
Tapp Thomas L.
Eppele Kyle
Jensen Nathan O.
Rockwell Collins, Inc.
Vo Don N.
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