Real-Time Channel Estimation Based on Fuzzy Logic

F Arani, P Coulton, B Honary

Abstract - The use of real time channel estimation information is known to result in significant performance advantages in coded systems operating on fading channels. Little work has been done in fast and accurate channel signal to noise ratio estimation in a very noisy channels (SNR<O dB). In this paper, a new real time channel estimation technique using the Viterbi algorithm and FUZZY logic concepts is presented.

1. INTRODUCTION

The distortion imposed by the channel on the transmitted data stream in a digital communication system is normally observed in the form of errors at the receiver. The main objectives of any communication system are to minimise the number of these errors and to maximise the throughput of the system. In order to optimise the system performance adaptively in response to channel conditions, an estimate of the receiver's error rate is required to initiate control actions.

Real time channel estimation techniques [ 11 are useful tools for obtaining an on-line estimate of the channel state. Previous work [2] in this area could not accurately estimate channel S N R fast under very noisy condition.

As a by-product of the Viterbi decoding algorithm, the cumulative metric of the most likely path through the decoder trellis is available as an additional information besides the decoded output symbol. This information may be interpreted as a measure for the signal-to-noise ratio (SNR) in the transmission channel [3] and consequently the error probability of the decoded sequence could be estimated.

In the channel estimation scheme described here, the path metric values at the output of the Viterbi decoder are applied to a Fuzzy-logic unit, which r e~eves this information by means of post- processing and mapping into membership functions @E). Channel S N R estimation is made after a fixed number of decoding steps.

2. THE FUZZY-LOGIC UNIT -

After a fixed number of decoding steps the Fuzzy-Logic unit (FLU) reads the transformed trellis side-infomation and computes the membership-values for each SNR-membership function in steps of 1dB in a range between - 7dB and 27dB. The rule base consists of a small look-up table, which contains the mean values of the input information obtained during off-line training for SNRs between -10 to 30dB in steps of 1 dB. Each membership function is triangular shaped, where the highest membership-value is assigned to the Fuzzy input being equal to the stored mean value for the kth dB step, with k {-7, -6, ... , 27). After comparing the input values with the rule-base, a vector of membership-values is obtained, which represents a fuzzy description for the S N R estimation. In order to reduce the variance of the channel SNFt estimate, the membership-values are defuzzificated by the Centroid inference method [4.5].

Lancaster Communications Research Center, Lancaster University, LA1 4YR

10/1 2 1996 The Institution of Electrical Engineers. 'rinted and published by the IEE. Savoy Place, London WCPR OBL, UK.

3. SIMULATION RESULTS

Simulation results have shown that after receiving 2kbit of decoded output symbols (i.e. eight taps), the estimated value for transmission EbiNo - between 0 dB and 25 dE3 can be obtained with 100% certainty with variance of 0.25 dl3.

4. REFERENCES

[ 11 F.Zolghadry B.Honary, M.Darnel1, ‘‘Statistical real-time channel evaluation techniques using variable rate T-codes”, IEE b e . , vol. 136, Pt. I, No. 4, 1989

M.Shaw, B.Honary, M.Damel1, “An RTCE assisted ARQ transmission Scheme: Design and Implementation”, IEE Cod. Proc. on HF Gonununication, vol. 284, pp.43-53,1988

J.G.Proakis, “Digital Communications”, 2nd ed., Mcktw-Hill 1989

[2]

[3]

[4] H.J.Zi”ermaq “Fuzzy set theory and its applications””, 2nd ed., Kluwer Academic Publishers, 1992

H.H.Bothe, “Fuzzy Logic”, Springer Verlag, 1993 [5]

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