Decision-aided equalizer

Details Decision-aided equalizer Decision-aided equalizer

2000-02-22

2004-02-10

Deppe, Betsy L. (Department: 2634)

Pulse or digital communications

Equalizers

Automatic

Reexamination Certificate

active

06690723

ABSTRACT:

FIELD OF THE INVENTION
This invention relates to decision-aided equalizers for communication receivers, and more particularly to decision-aided equalizers with reduced error propagation.
BACKGROUND OF THE INVENTION
In modern communications systems employing Time Division Multiple Access such as, for example, the GSM radiotelephone system, digital information in the form of a bit stream representing voice or data is encoded by the transmitter into a series of complex phase shifted signals and is transmitted by modulating the carrier wave of the transmitter with the encoded symbols. Each symbol transmitted represents a series of bits from the data stream. For instance, eight different such symbols may be used, each symbol representing three bits of the data stream.
The transmitted symbols are received by a receiver and the symbols are reconverted into a bit stream. If the transmission and reception are free from interference and distortion, the symbols are readily convened into a bit stream representing exactly the original bit stream before its conversion into symbols by the transmitter. As a practical matter, however, transmissions are rarely distortion free. Among the distortions which occur is multipath distortion caused when the receiver receives two representation of the same transmitted signal which, because of reflections, arrive at the receiver at slightly different times. The reception of two time-skewed signals causes Intersymbol Interference (ISI), where a symbol's phase component may be distorted by the delayed arrival of an earlier transmitted symbol. Channel fading and noise can also cause difficulty in accurately receiving transmitted signals.
Both of these phenomena can significantly degrade the performance of a high-speed wireless communication system
Intersymbol interference (ISI) introduced by multipath propagation is mitigated in the receiver of a communication system by means of an Equalizer. Classically, two approaches are considered for this stage: either a symbol-by-symbol decision is performed, based on a filtered received signal, or the whole received sequence is estimated, often using the so-called Viterbi algorithm implementing the Maximum Likelihood Sequence Estimator (MLSE solution).
For symbol-by-symbol decisions, the Decision Feedback Equalizer (DFE) realizes an interesting trade-off between complexity and performance. The best bit error rate (BER) performance is obtained when the DFE filters are optimized for the MMSE (Minimum Mean Square Error) criterion. However, the DFE structure suffers from the error propagation phenomenon: if an error occurs, it enhances the ISI on the following samples entering the decision device, thus causing in turn more errors.
On the other hand, sequence estimation methods give the best performance, but at the cost of increased complexity. For an eight-state symbol constellation, for example, a Viterbi calculation must calculate 8
L−1
possibilities, where L is the number of taps in the channel. Thus suboptimal techniques for the MLSE were proposed:
In “Delayed Decision-Feedback Sequence Estimation” (A. Duel-Hallen, C. Heegard, IEEE Transactions on Communications, vol.37 No: 5, May 1989), the state trellis, basis for the Viterbi algorithm, only takes into account the first N taps in the channel. Then, the transition metrics are computed using past tentative decisions on each of the summing paths, In this respect, it is said that such sequence estimation incorporates a feedback, and the resulting Decision Feedback Sequence Estimation (DFSE) algorithm has a reduced number of states compared to the MLSE.
In “Reduced-State Sequence Estimation with Set Partitioning and Decision Feedback” (M. Vedat Eyuboglu, S. U. H. Quereshi, IEEE Transaction on Communications, vol.36, No. 1, January 1988), further reduction of the number of states can be achieved by introducing a partitioning of the constellation symbols. This approach is referred to as reduced state sequence estimation (RSSE). There, a “reduced” state is a vector of indexes of symbol subsets. This algorithm provides the greatest flexibility when handling Finite Impulse Response (FIR) channels and high order modulations.
The advantage of reduced state approaches is to reduce the complexity of the Viterbi-based equalizers. On the other hand, their performance can also be degraded by error propagation, even if this phenomenon is less severe than in the DFE, since the reliability of the feedback decisions is improved by the Viterbi stage. This error propagation effect is minimized when the channel to be equalized is minimum-phase. In a mobile communication context, this is usually not the case.
With a reduced state Viterbi processor, typically the input symbol stream is filtered in some manner and applied to the processor so that error propagation is reduced. Other inputs to the Viterbi processor are the estimated channel coefficients, which are usually obtained by calculating the channel parameters from a training frame of a known symbol sequence which is transmitted as a part of a preamble to the data. The calculation determines the weights to be ascribed to each of the coefficients of the preceding symbols so that the weighted effect of the preceding symbols can be subtracted from the combined signal to obtain the value of the symbol being calculated. The Viterbi processor then calculates hard outputs, which are used in the feedback path of the processor in the reduced state version.
Interleaving, and channel coding also can be used to mitigate the adverse effects of multipath and fading. A decision-aided equalizer, such as a decision-feedback equalizer (DFE), can be used as a means for mitigating the effects of ISI in a system where an interleaves and channel coder provide further protection against channel fading, residual ISI, and thermal noise.
In general, the decision-aided equalizer uses the linear combination of the past decisions to cancel post-cursor ISI, where the feedback filter (FBF) coefficients are obtained under the assumption that past decisions are correct. Consequently, errors in the past decisions enhance, instead of cancel, the ISI. The enhanced ISI may cause more decision errors in the current and future symbols, which will in turn further enhance the ISI, Thus leading again to propagation errors.
SUMMARY OF THE INVENTION
To address the shortcomings of the prior art, applicants have provided an advantageous new hybrid equalizer and and an improved method for equalizing to mitigate the effects of intersymbol interference, channel fading and noise.
A method for channel equalization in a communications receiver comprises receiving an input data stream comprising modulation symbols; and employing a reduced set sequence estimation algorithm to produce intermediate hard decisions based on the input data stream and on a channel reference. The intermediate hard decisions are applied to a decision feedback filter in a feedback loop. In addition, soft output sequences of symbols are produced based on the input data stream, which are decoded, error-corrected and re-encoded. The error-corrected soft output sequences are then applied iteratively to the decision feedback filter, in order to generate a channel equalized received data stream based on both the feedback soft output sequences and the intermediate hard decisions.
An iterative decision-aided equalizer comprises a decision feedback equalizer having a processor employing a reduced set sequence estimation algorithm for receiving an input data stream comprising modulation symbols, and providing intermediate hard decisions based on the input data stream and on a channel reference. A decision feedback filter is coupled to an output of the processor A summing device sums an output of the decision feedback filter with the input data stream to produce soft outputs representing the received symbols. A feedback loop receives soft outputs from the summing device and iteratively provides error-corrected soft outputs to the decision feedback filter. The feedback loop comprises a decoder for decoding and er

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