Telecommunications – Transmitter and receiver at separate stations – Having measuring – testing – or monitoring of system or part
Reexamination Certificate
1999-04-29
2002-05-21
Chin, Wellington (Department: 2664)
Telecommunications
Transmitter and receiver at separate stations
Having measuring, testing, or monitoring of system or part
C370S335000, C370S342000
Reexamination Certificate
active
06393257
ABSTRACT:
FIELD OF THE INVENTION
The invention relates to communication systems. More particularly, the invention relates to methods and apparatus for improving reception and decoding of signals encoded using turbo codes.
DESCRIPTION OF THE RELATED ART
Wireless communication signals typically suffer from more interference and noise than wired communications. Additionally, there is a need to provide numerous channels over a given bandwidth. As a result, numerous encoding techniques have been developed, such as code division multiple access (CDMA). CDMA techniques in a communication system are disclosed in U.S. Pat. No. 4,901,307, entitled “SPREAD SPECTRUM MULTIPLE ACCESS COMMUNICATION SYSTEM USING SATELLITE OR TERRESTRIAL REPEATERS,” and U.S. Pat. No. 5,103,459, entitled “SYSTEM AND METHOD FOR GENERATING SIGNAL WAVEFORMS IN A CDMA CELLULAR TELEPHONE SYSTEM,” both assigned to the assignee of the present invention.
CDMA modulation techniques can provide capacity improvements over other techniques, such as time division multiple access (TDMA) and frequency division multiple access (FDMA) based in part on CDMA's use of orthogonal functions or codes. Additionally, CDMA receivers employ Viterbi decoders that employ Viterbi algorithms to perform maximum likelihood decoding of received signals.
However, even Viterbi decoding suffers from decoding errors when receiving signals over a noisy channel. A new encoding scheme employing “turbo” codes employs a combination of two simple encoders that receive a block of K information bits, and which generate parity symbols from two simple recursive convolutional codes, each having a small number of states. The K information bits are sent uncoded, with the parity symbols, over a noisy channel. Importantly, an interleaver permutes the original K information bits before inputting such bits to the second encoder. The permutation causes one encoder to produce low-weight code words, while the other encoder produces high-weight code words. The resulting code is similar to “random” block codes with K information bits. Random block codes are known to achieve Shannon-limit performance when K is large, but truly random block codes require a prohibitively costly and complex decoding algorithm.
At the receiver, a pair of simple decoders employing iterative maximum a posteriori algorithms receive both the original K information bits, together with one of the sets of parity symbols from one of the two encoders. The decoders are individually matched to the simple codes, and each decoder sends an a posteriori likelihood estimate of the decoded bits to the other decoder, while using the corresponding estimates from the other decoder as an a priori likelihood. Uncommon information bits corrupted by the noisy channel are available to each decoder to minimize the a priori likelihoods, while the decoders use maximum a priori decoding algorithms, which requires the same number of states as the Viterbi algorithm. The two constituent decoders perform several iterations until reaching satisfactory convergence, at which point the final output is a hard-quantized version of the likelihood estimates of either of the decoders. Further details on turbo codes may be found, for example, in “A Primer On Turbo Code Concepts,” by B. Sklar,
IEEE Communications Magazine
, 35:12 (December 1997).
Academic results have shown that turbo codes have the potential to improve information transmission over a noisy channel by a large amount, compared to classical Viterbi decoding, even results amazingly close to the theoretical Shannon-limit. Unfortunately, such results suppose idealized conditions at the receiver, which are in practice, very difficult to approach. One troublesome idealized condition is that the receiver is presumed to know the signal and noise power on a per symbol basis. This can be particularly difficult because the signals are multiplied by information bits, which makes the means or averages of the signals zero, thus disallowing usual averaging procedures for estimating signal noise power.
In one article, entitled “SNR Mismatch And Online Estimation In Turbo Decoding,” by T. Summers and S. Wilson,
IEEE Transactions on Communications
46:4 (April 1998), the authors note that iterative decoding of turbo codes, as well as other similar concatenated coding schemes, require knowledge of the signal-to-noise ratio (SNR) of the channel so that proper blending of the a posteriori information of the separate decoders is achieved. In this article, the authors study the sensitivity of decoder performance to misestimation of the SNR, and propose a scheme that estimates the unknown SNR from each code block, before decoding. This is for the additive white gaussian noise (AWGN) channel. However, such an approach does not provide the individual estimates of signal and noise, needed for turbo codes in fading channels. Indeed, without good channel estimation, turbo encoding is much less attractive, if not worse, when compared to conventional Viterbi decoding of a convolutional code.
The inventors have found, through experimentation, that classical techniques for estimating channel conditions induce a loss in performance because of the poor performance of such estimation techniques. The inventors have found a new set of techniques for signal and noise power estimation that apply not only to turbo codes, but to applications in other fields where such type of estimation is needed (e.g., power control in CDMA telecommunications systems, other concatenated coding schemes, etc.). Such new techniques estimate the signal to noise ratio in received signals, and importantly, provide estimations of signal and noise separately, and thereby provide important improvements over the method described in the Summers and Wilson article. Moreover, one aspect of the invention provides initial estimates of signal and noise, such as by using an efficient curve fitting technique. Thereafter, the energy of received pilot symbols is used to provide a finer estimation of the signal and noise. Thus, aspects of the present invention overcome problems in prior systems, and provide additional benefits, as those skilled in the relevant art will appreciate from the following discussion.
In a broad sense, aspects of the invention include a method for estimating channel conditions of received signals. The method includes: (a) receiving a signal encoded with concatenated codes over a channel having noise, wherein the received signal has a certain amplitude; (b) estimating a certain amplitude based on the received signal; and (c) separately estimating a variance &sgr; of the noise, based on the received pilot signal.
REFERENCES:
patent: 4901307 (1990-02-01), Gilhousen et al.
patent: 5103459 (1992-04-01), Gilhousen et al.
Valenti et al., “Performance of Turbo Codes in Interleaved Flat Fading Channels with Estimated Channel State Information”, IEEE VTC 1998, Ottawa, Canada, pp. 66-70.*
Valenti et al, “Refined channel estimation for coherent detection of turbo codes over flat-fading channels”, Electronics Letters, GB, IEE Stevenage, vol. 34, No. 17, pp. 1648-1649.*
1997 IEEE Communications Magazine, vol. 35, No. 12, “A Printer On Turbo Code Concepts”, B. Sklar, pp. 94-102.
1997 International Conf. Telecommunications, “A Novel Variance Estimator for Turbo-Code Decoding”, M. Reed et al., pp. 173-178.
1998 IEEE Transactions on Communications, vol. 46, No. 4, “SNR Mismatch and Online Estimation in Turbo Decoding”, T. Summers et al., pp. 421-423.
Baker Kent D.
Chin Wellington
Qualcomm Incorporated
Rouse Thomas R.
Tran Maikhanh
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