Error detection/correction and fault detection/recovery – Pulse or data error handling – Digital data error correction
Reexamination Certificate
1999-05-28
2002-05-14
Decady, Albert (Department: 2133)
Error detection/correction and fault detection/recovery
Pulse or data error handling
Digital data error correction
C714S821000
Reexamination Certificate
active
06389572
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method for extracting bits with minimal errors from a modulated waveform by using information from the entire transmitted data block to remove intersymbol interference from the decoded data.
2. Description of Related Art
Digital signal processing (DSP) refers to the various techniques for improving the accuracy and reliability of digital communications. The developed theories and mathematical solutions behind DSP are quite complex and beyond the scope of this description. However, the basic premise behind DSP involves clarifying, or standardizing, the levels or states of a digital signal. A DSP circuit is able to differentiate between human-made signals, which are orderly, and noise, which is inherently random or chaotic.
All communications circuits contain some noise. Such is the case whether the signals are analog or digital, and regardless of the type of information conveyed. Practitioners in the field are continually striving to find new ways to improve the signal-to-noise (S/N) ratio in communications systems. Traditional methods of optimizing the S/N ratio include increasing the transmitted signal power and increasing the receiver sensitivity. In wireless systems, specialized antenna configurations are also used. Digital signal processing dramatically improves the sensitivity of a receiving unit. The effect is most noticeable when noise competes with a desired signal. A good DSP circuit can greatly improve the S/N ratio, but there are limits as to what the circuit can do. If the noise is so strong that all traces of the signal are obliterated, then a DSP cannot find any order in the chaos, and no signal will be received.
If the incoming signal is analog, for example from a standard television broadcast station, the signal is first converted to digital form by an analog-to-digital converter (ADC). The resulting digital signal has two or more levels. Ideally, these levels are always predictable voltages or currents. However, because the incoming signal contains noise, the levels are not always at the standard values. In general terms, the DSP adjusts the levels so that they are at the correct values. This essentially eliminates the noise components in the signal. The DSP acts directly on the incoming signal, thereby eliminating irregularities caused by noise, and thereby minimizing the number of errors per unit time.
The proliferation of portable computers and computing devices in today's society has increased the demand for transmission of data over wireless links. Binary data, composed (for instance) of sharp “one to zero” and “zero to one” transitions, results in a spectrum rich in harmonic content that is not well suited to radio frequency (RF) transmission. Hence, the field of digital modulation has been providing various transmission solutions. Recent standards such as Cellular Digital Packet Data (CDPD) and Mobitex (Mobitex is a trademark owned by Telia Corporation) specify Gaussian filtered Minimum Shift Keying (GMSK) for their modulation method. Other modulation techniques include frequency shift keying (FSK), multi-level frequency shift keying (MFSK), continuous phase frequency shift keying (CPFSK), minimum shift keying (MSK), tamed frequency modulation (TFM), phase shift keying (PSK), quadrature phase shift keying (QPSK), differential quadrature phase shift keying (DQPSK), Pi/4 DQPSK, quadrature amplitude modulation (QAM).
Referring now to
FIG. 1
a
, a radio station
110
is shown transmitting a signal
112
to a receiving device
114
. The modulation technique shown includes GMSK
116
with a BT of 0.3. Typical data rates of the link include 8000 bps (bits per second), which translates to a bandwidth of approximately 2400 Hz. The frequency affects the amount of intersymbol interference (ISI) associated with the signal.
FIG. 1
b
shows a block diagram of the data sequence
120
which generally comprises such a transmission. The first 16 bits are bit sync information
122
. The second 16 bits are frame sync information
124
. The next 24 bits are the frame head. The last sequence of bits comprise the data blocks
126
, which might number from 1 to 32.
FIG. 1
c
shows a representative data block
130
which includes 12 columns (
132
) by 20 rows (
134
) of data bits. Each row includes 8 data bits
136
and 4 parity bits
138
, which further represents a (
12
,
8
) Hamming code. 18 bytes are generated and then the final two bytes
140
and
142
are CRC's (or cyclic redundancy checking).
FIG. 2
shows a block diagram
200
for generating and providing error correction on a data block transmitted via a modulation technique. An analog input
202
is fed into an ADC
204
to produce an 8 bit data value. An equalizer
206
typically applies 16 bit DSP operations on the incoming data value to remove ISI. DSP equalizer systems have become ubiquitous in many diverse applications including voice, data, and video communications via various transmission media. Typical applications range from acoustic echo cancelers for full-duplex speaker phones to video deghosting systems for terrestrial television broadcasts to signal conditioners for wireline modems and wireless telephony. Equalizer schemes include, for example, decision feedback equalizers (DFE), zero forcing equalizers, and minimum mean square error equalizers (MMSE). The effect of an equalization system is to compensate for transmission-channel impairments such as frequency-dependant phase and amplitude distortion. If the channel impulse response can be defined, a filter (such as FIR, or IIR) can be implemented to counter the ill effects of the channel. The 16 bits of DSP data are then fed into a detector
208
which produces a 1 bit result used to build the data block. Thereafter Forward Error Correction (FEC) is applied, and the CRC's are used to verify whether the data was received correctly.
FIG. 3
shows an example plot
300
of waveforms for pulsed information which have been transmitted over an analog channel such as a phone line or airwaves. Even though the original signal is a discrete time sequence, the received signal is a continuous time signal. Heuristically, the channel might be considered to act as an analog low-pass filter, thereby spreading or smearing the shape of the impulse train into a continuous signal whose peaks relate to the amplitudes of the original pulses. Mathematically, the operation can be described as a convolution of the pulse sequence by a continuous time channel response. The resulting signal is a function of time (t) and the symbol period (T). The signal consists of the sum of many scaled and shifted continuous time system impulse responses. The impulse responses are scaled by the amplitudes of the transmitted pulses.
In
FIG. 3
, a data transmission of example bits
1
,
0
,
1
might be represented by the summation of the three waveforms
302
,
304
, and
306
. Each of the waveform elements (e.g.
302
) could be represented, in its simplest form, as a +n or −n level spanning only a single symbol period T. However, this manner of transmission uses significant bandwidth. To get more information, but use less bandwidth, the signal element will instead be made to spread out and span (for example) 3 symbol periods. At each sample period, the various waveforms are summed to produce the resulting waveform. When ISI is present, the symbols interfere with each other and create difficulties in reconstructing the summed wave. ISI is particularly difficult to remove for sample periods (e.g.
308
) where 2 or more waveforms are providing contributions to the summed result.
Prior solutions have used the equalizer element to attempt to remove ISI effects from the modulated data. The ultimate design and functionality of the equalizer depends, among other things, upon proper modeling of the channel impulse response. The equalizer then derives subtraction components at each of the sampling points to produce the proper waveform result. Equalizer design has proven to be difficult, of
Garrabrant Gary Walter
Vertatschitsch Edward Joseph
Chase Shelly A
De'cady Albert
Hickman Palermo & Truong & Becker LLP
Mahamedi Van
Palm Inc.
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