An Evolutionary Approach for Joint Blind Multichannel Estimation and Order Detection
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An Evolutionary Approach for Joint Blind Multichannel Estimation and Order Detection Chen Fangjiong Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong Department of Electronic Engineering, South China University of Technology, Wushan, Guangzhou 510641, China Email: [email protected]
Sam Kwong Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong Email: [email protected]
Wei Gang Department of Electronic Engineering, South China University of Technology, Wushan, Guangzhou 510641, China Email: [email protected] Received 30 May 2001 and in revised form 28 January 2003 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. Keywords and phrases: genetic algorithms, SIMO, blind signal identification.
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INTRODUCTION
Many applications in signal processing encounter the problem of blind multichannel identification. Traditional methods of such identification usually apply higher-order statistics techniques. The major problems of these methods are slow convergence and many local optima [1]. Since the original work of Tong et al. [1, 2], many lower-order statisticsbased methods have been proposed for blind multichannel identification (see [3] and references therein). A common assumption in these methods is that the channel order is known in advance. However, such information is, in fact, not available. Thus, we are obliged to estimate the channel order beforehand. 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]. To tackle this drawback, a class of channel-estimation algorithms performing joint order detection and parameter estimation has been proposed [5, 6]. In [5], a cost function in-
cluding channel order and parameters is proposed. However, the algorithm may not be efficient because the channel order is estimated by evaluating all the possible candidates from 1 to a predefined ceiling. The method proposed in [6] is also not a real joint approach since the order was separately estimated by detecting the rank of an overmodelled data matrix. In fact, this is very similar to the methods that applied a rankdetection procedure to an overmodelled data covariance matrix in [4]. Order estimation via rank detection may not be
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