Abstract

A joint blind order-detection and parameter-estimation algorithm for a single-input multiple-output (SIMO) channel is presented. Based on the subspace decomposition of the channel output, an objective function including channel order and channel parameters is proposed. The problem is resolved by using a specifically designed genetic algorithm (GA). In the proposed GA, we encode both the channel order and parameters into a single chromosome, so they can be estimated simultaneously. Novel GA operators and convergence criteria are used to guarantee correct and high convergence speed. Simulation results show that the proposed GA achieves satisfactory convergence speed and performance.

Highlights

  • Many applications in signal processing encounter the problem of blind multichannel identification

  • We propose a real joint order-detection and channel-estimation method based on genetic algorithm (GA)

  • In the proposed GA, though there is no guarantee that the order chromosomes are absolutely converging on the real channel order in the first inner loop run, we have proposed several strategies to make them converge more closely

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Summary

INTRODUCTION

Many applications in signal processing encounter the problem of blind multichannel identification Traditional methods of such identification usually apply higher-order statistics techniques. Though many order-detection algorithms can be applied (e.g., see [4]) to solve this particular problem, the approaches that separate order detection and parameter estimation may not be efficient, especially when the channelimpulse response has small head and tail taps [5]. The method proposed in [6] is not a real joint approach since the order was separately estimated by detecting the rank of an overmodelled data matrix. This is very similar to the methods that applied a rankdetection procedure to an overmodelled data covariance matrix in [4]. Simulation results show that the proposed GA achieves a similar performance

PROBLEM FORMULATION
GENETIC ALGORITHM
EXPERIMENTAL RESULT
Findings
CONCLUSIONS

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