Soft decision output generator

Details Soft decision output generator Soft decision output generator

2001-01-04

2004-05-04

Tran, Khai (Department: 2631)

Pulse or digital communications

Receivers

Particular pulse demodulator or detector

Reexamination Certificate

active

06731700

ABSTRACT:

FIELD OF THE INVENTION
The present invention relates generally to communication systems and more particularly relates to an apparatus for and a method of generating soft decision information for symbols received over a channel.
BACKGROUND OF THE INVENTION
In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 500 million users that subscribe to cellular telephone services and the number is continually increasing. Eventually, in the not too distant future the number of cellular subscribers will exceed the number of fixed line telephone installations. Already, in many cases, the revenues from mobile services already exceeds that for fixed line services even though the amount of traffic generated through mobile phones is much less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs) and wireless local loops (WLLs). In addition, new broadband communication schemes are rapidly being deployed to provide users with increased bandwidth and faster access to the Internet. Broadband services such as xDSL, short range high speed wireless connections, high rate satellite downlink (and the uplink in some cases) are being offered to users in more and more locations.
In connection with cellular services, the majority of users currently subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically second generation digital technology. Currently, third generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM, TDMA (IS-136) or CDMA (IS-95), for example.
A diagram illustrating an example prior art communication system employing an inner and outer encoder in the transmitter, inner and outer decoding stages in the receiver and a noise source after the channel is shown in FIG.
1
. The communication system, generally referenced
10
, represents the typical scheme that may be used in providing many of the communication services described above. In such a system, the transmitter
11
comprises an encoder
14
, symbol generator
16
and modulator
18
. Input data bits
12
to be transmitted are input to the encoder
14
, which may comprise an error correction encoder such as a Reed Solomon encoder, a convolutional encoder, a parity bit generator, etc. The encoder functions to add redundancy bits to enable errors in transmission to be located and corrected.
It is noted that both the inner and outer decoders in the receiver have complementary encoders in the transmitter. The outer encoder in the transmitter comprises the encoder
14
, e.g., Reed Solomon, etc. The inner encoder comprises the channel
20
which often times can be modeled as an L-symbol long FIR-type channel.
The bits output of the encoder are then mapped to symbols by the symbol generator
16
. The symbol generator functions to transform the bits to modulator symbols. For example, an 8-PSK modulator converts input bits into one of eight symbols. Thus, the symbol generator generates a symbol for every three input bits.
The output from the mapper is input to the modulator which receives symbols in the M-ary alphabet and generates the analog signal subsequently transmitted over the channel
20
. The channel may comprise a mobile wireless channel, e.g., cellular, cordless, a fixed wireless channel, e.g., satellite, or may comprise a wired channel, e.g., xDSL, ISDN, Ethernet, etc. The processing performed in the transmitter is intended to generate a signal that can be transmitted over the channel to provide robust, error free detection by the receiver.
At the receiver
13
, the analog signal from the channel is input to front end circuitry
22
which demodulates and samples the received signal to generate received samples y(k)
21
. The samples are first input to an inner decoder
24
. An example of an inner decoder is an equalizer which compensates for the ISI caused by the delay and time spreading of the channel. Examples of commonly used types of equalizers include the maximum likelihood sequence estimation (MLSE) equalizer that utilizes the well known Viterbi Algorithm (VA), linear equalizer and decision feedback equalizer (DFE). The function of the equalizer is to detect the symbols that were originally transmitted by the modulator.
The output of the inner decoder comprises symbols s(k)
23
which represent hard decisions. The hard decisions are then input to an outer decoder
26
which functions to locate and fix errors using the redundancy inserted by the encoder. The outer decoder generates the binary receive data.
An example of an outer decoder is a convolutional decoder that utilizes the Viterbi Algorithm. The Viterbi algorithm is widely used in communication systems and has been adapted to perform functions including demodulation, decoding, equalization, etc. Many systems utilize the Viterbi Algorithm in both the inner and outer decoding stages.
As described above, the outer decoder, in some systems, is adapted to utilize the hard decisions output from the inner decoder, e.g., the equalizer. Optimal decoders, on the other hand, require soft decision inputs. For example, an outer decoder that utilizes the Viterbi Algorithm to perform convolutional forward error correction decoding, requires soft decisions as input. The advantage of a Viterbi decoder is that it can efficiently process soft decision information.
One drawback of such a system is that in some cases the outer decoder is very sensitive to error bursts produced by the inner decoder. A second drawback arises when the inner decoder cannot provide soft decision information and thus only provides hard decisions to the outer decoder. In such a system, the performance of the outer stages of a concatenated coding system is substantially lower than that of a system wherein the inner decoder is capable of generating soft decision information.
The problem is illustrated by considering a receiver adapted to handle a GSM or GERAN signal. Such a system utilizes convolutional coding for performing Forward Error Correction (FEC) over channels that require equalization. The equalizer and outer FEC decoder typically used employ the Viterbi Algorithm in their operation. The output of the equalizer, however, only produces hard decisions which leads to reduced performance of the outer VA convolutional FEC decoder.
There exist a class of decoders that provide improved performance by utilizing soft information about the received symbols rather than only hard decisions. Examples include turbo decoders and soft decision convolutional decoders utilizing the Viterbi Algorithm, etc. This class of decoders provides better performance by taking into account soft information about the reliability of received symbols. The improved performance of the decoder cannot be realized, however, when soft information about the received symbols is not available.
The problem of error bursts can be eliminated by the use of interleaving in the transmitter and de-interleaving in the receiver, between the inner and the output decoders. The second problem can only be eliminated by providing an inner decoder capable of generating soft decision information.
Several prior techniques have been developed to provide soft symbol decisions from the inner decoder (or equalizer) that can be used by a soft decoder. Most of these soft output equalizer techniques are based on maximum likelihood sequence estimation (MLSE) or computational complex methods such as maximum a posteriori (MAP) algorithms. A few of these techniques are described below.
One technique uses an optimum soft output algorithm derived under the constraint of fixed de

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